awesome-machine-learning-atomistic-simulation
An overview of literature that discusses the use of machine learning for atomistic simulations
https://github.com/m-k-S/awesome-machine-learning-atomistic-simulation
Last synced: 15 days ago
JSON representation
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Embeddings and Representations
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Atom-Centered Symmetry Functions (ACSF)
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2
- Amp: A modular approach to machine learning in atomistic simulations
- Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species
- wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
- Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems
- Augmenting machine learning of energy landscapes with local structural information
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- Unified theory of atom-centered representations and message-passing machine-learning schemes
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
- Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
- Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems
- Augmenting machine learning of energy landscapes with local structural information
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- Unified theory of atom-centered representations and message-passing machine-learning schemes
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
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Meta-literature
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
- A Performance and Cost Assessment of Machine Learning Interatomic Potentials
- DScribe: Library of descriptors for machine learning in materials science
- Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned interatomic potentials
- Sensitivity and Dimensionality of Atomic Environment Representations used for Machine Learning Interatomic Potentials
- An assessment of the structural resolution of various fingerprints commonly used in machine learning
- Incompleteness of Atomic Structure Representations
- Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions
- The role of feature space in atomistic learning
- Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations
- Compressing local atomic neighbourhood descriptors
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned interatomic potentials
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned interatomic potentials
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
- The role of feature space in atomistic learning
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Miscellaneous
- Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks
- Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks
- Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
- How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
- A fingerprint based metric for measuring similarities of crystalline structures
- Unified Representation of Molecules and Crystals for Machine Learning
- An Atomistic Fingerprint Algorithm for Learning Ab Initio Molecular Force Fields
- A novel approach to describe chemical environments in high-dimensional neural network potentials
- Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation
- Continuous and optimally complete description of chemical environments using Spherical Bessel descriptors
- TUCAN: A molecular identifier and descriptor applicable to the whole periodic table from hydrogen to oganesson - Pawlis, Schatzschneider | Mar 2022 |
- A novel approach to describe chemical environments in high-dimensional neural network potentials
- Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks
- A fingerprint based metric for measuring similarities of crystalline structures
- A novel approach to describe chemical environments in high-dimensional neural network potentials
- Continuous and optimally complete description of chemical environments using Spherical Bessel descriptors
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Atomic Cluster Expansion (ACE)
- Atomic cluster expansion for accurate and transferable interatomic potentials
- Atomic cluster expansion of scalar, vectorial and tensorial properties and including magnetism and charge transfer
- Performant implementation of the atomic cluster expansion: Application to copper and silicon
- Efficient parametrization of the atomic cluster expansion
- Atomic cluster expansion: Completeness, efficiency and stability
- Multilayer atomic cluster expansion for semi-local interactions
- Atomic cluster expansion and wave function representations
- Permutation-adapted complete and independent basis for atomic cluster expansion descriptors
- Atomic cluster expansion: Completeness, efficiency and stability
- Multilayer atomic cluster expansion for semi-local interactions
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Smooth Overlap of Atomic Position (SOAP)
- On representing chemical environments
- Comparing molecules and solids across structural and alchemical space
- Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
- Machine-learning of atomic-scale properties based on physical principles
- Atom-density representations for machine learning
- Atomic-scale representation and statistical learning of tensorial properties
- Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials
- Efficient implementation of atom-density representations
- Atom-density representations for machine learning
- Atom-density representations for machine learning
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Gaussian Approximation Potentials (GAP)
- see here
- Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
- Gaussian Approximation Potential: an interatomic potential derived from first principles Quantum Mechanics
- Accuracy and transferability of GAP models for tungsten
- Localized Coulomb Descriptors for the Gaussian Approximation Potential
- Many-Body Coarse-Grained Interactions using Gaussian Approximation Potentials
- Machine-learned Interatomic Potentials for Alloys and Alloy Phase Diagrams
- Combining phonon accuracy with high transferability in Gaussian approximation potential models
- Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
- Massively Parallel Fitting of Gaussian Approximation Potentials
- Atomistic structure search using local surrogate mode
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Moment Tensor Potentials (MTP)
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FCHL
- Alchemical and structural distribution based representation for universal quantum machine learning
- FCHL revisited: Faster and more accurate quantum machine learning
- Alchemical and structural distribution based representation for universal quantum machine learning
- FCHL revisited: Faster and more accurate quantum machine learning
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Wavelet Scattering
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Tools and Architectures
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SchNet
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
- SchNet – a deep learning architecture for molecules and materials
- Analysis of Atomistic Representations Using Weighted Skip-Connections
- SchNetPack: A Deep Learning Toolbox For Atomistic Systems
- Learning representations of molecules and materials with atomistic neural networks
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics
- A deep neural network for molecular wave functions in quasi-atomic minimal basis representation
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- SchNet – a deep learning architecture for molecules and materials
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
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Symmetric Gradient Domain Machine Learning (sGDML)
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Machine learning of accurate energy-conserving molecular force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
- sGDML: Constructing accurate and data efficient molecular force fields using machine learning
- Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
- Accurate Molecular Dynamics Enabled by Efficient Physically-Constrained Machine Learning Approaches
- Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach
- Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
- Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules - Galindo, Chmiela, Poltavsky, Tkatchenko | Sep 2022 |
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
- Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach
- Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
- Towards exact molecular dynamics simulations with machine-learned force fields
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Miscellaneous
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
- Cormorant: Covariant Molecular Neural Networks
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
- Equivariant message passing for the prediction of tensorial properties and molecular spectra
- ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry - Equi) | Qiao, Christensen, Welborn, Manby, Anandkumar, Miller III | May 2021 |
- GemNet: Universal Directional Graph Neural Networks for Molecules
- Rotation Invariant Graph Neural Networks using Spin Convolutions
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Convergence acceleration in machine learning potentials for atomistic simulations
- NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics - Rial, Doerr, Fino, Eastman, Markland, Chodera, Fabritiis | Jan 2022 |
- TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials
- A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
- Equivariant Graph Attention Networks for Molecular Property Prediction
- Learning Atomic Multipoles: Prediction of the Electrostatic Potential with Equivariant Graph Neural Networks
- NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces - Gordon, Bertels, Hao, Leven, T. Head-Gordon | Feb 2022 |
- MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry - Equi) | Qiao, Christensen, Welborn, Manby, Anandkumar, Miller III | May 2021 |
- GemNet: Universal Directional Graph Neural Networks for Molecules
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials - Lanord, Wang, Shao-Horn, Grossman | Jun 2019 |
- A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
-
Meta-literature
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules - Galindo, Fonseca, Poltavsky, Tkatchenko | Mar 2021 |
- Symmetry Group Equivariant Architectures for Physics
- How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations?
- Forces Are Not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations - Bombarelli, Jaakkola | Sep 2022 |
- Neural Scaling of Deep Chemical Models - Bombarelli, Coley, Gadepally | May 2022 |
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules - Galindo, Fonseca, Poltavsky, Tkatchenko | Mar 2021 |
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules - Galindo, Fonseca, Poltavsky, Tkatchenko | Mar 2021 |
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
- Incompleteness of graph neural networks for points clouds in three dimensions
-
e3nn Precursor
- These works were developed independently of each other but propose very similar ideas which were mostly consolidated
- Group Equivariant Convolutional Networks
- Spherical CNNs
- Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
- N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials
- Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
- 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
- SE(3)-Equivariant prediction of molecular wavefunctions and electronic densities
- These works were developed independently of each other but propose very similar ideas which were mostly consolidated
-
e3nn
- Finding symmetry breaking order parameters with Euclidean neural networks
- Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties
- E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
- Direct Prediction of Phonon Density of States With Euclidean Neural Networks
- Cracking the Quantum Scaling Limit with Machine Learned Electron Densities
- Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
- The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
- MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
- Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
- e3nn: Euclidean Neural Networks
- Machine Learning Magnetism Classifiers from Atomic Coordinates
- High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks
- Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
-
Equivariant Graph Neural Networks (EGNN)
-
Preferred Networks
- TeaNet: universal neural network interatomic potential inspired by iterative electronic relaxations
- Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements
- Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements
-
DeepPot
- Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
- End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems - SE)| Zhang, Han, Wang, Saidi, Car, Weinan | May 2018 |
- DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
-
DimeNet
-
Sub Categories
Miscellaneous
401
Meta-literature
174
Atom-Centered Symmetry Functions (ACSF)
101
Symmetric Gradient Domain Machine Learning (sGDML)
90
SchNet
85
e3nn
13
Gaussian Approximation Potentials (GAP)
11
Atomic Cluster Expansion (ACE)
10
Smooth Overlap of Atomic Position (SOAP)
10
e3nn Precursor
9
FCHL
4
Wavelet Scattering
3
Moment Tensor Potentials (MTP)
3
Preferred Networks
3
Equivariant Graph Neural Networks (EGNN)
3
DeepPot
3
DimeNet
2