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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: 2 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
- 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
- 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
- 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
- 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
- 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
- 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
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Smooth Overlap of Atomic Position (SOAP)
- Atom-density representations for machine learning
- 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
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Miscellaneous
- Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks
- 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
- 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 |
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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
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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
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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
- 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
- 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
- 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
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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
- 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
- Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
- 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
- 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
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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 |
- 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 |
- 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
- 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
- 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 |
- 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 |
- 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
- 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
- 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 |
- 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
- 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
- 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
- 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
- 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
-
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
-
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
227
Meta-literature
100
Atom-Centered Symmetry Functions (ACSF)
60
Symmetric Gradient Domain Machine Learning (sGDML)
51
SchNet
49
e3nn
13
Gaussian Approximation Potentials (GAP)
11
Smooth Overlap of Atomic Position (SOAP)
9
Atomic Cluster Expansion (ACE)
8
e3nn Precursor
8
Wavelet Scattering
3
Moment Tensor Potentials (MTP)
3
Preferred Networks
3
Equivariant Graph Neural Networks (EGNN)
3
DeepPot
3
FCHL
2
DimeNet
2