awesome-quantum-chemistry
Quantum Chemistry is awesome. Throw your textbook in the garbage, light the garbage can on fire, and blend the ashes into your cold brew almond milk latte and read this.
https://github.com/twoXes/awesome-quantum-chemistry
Last synced: 9 days ago
JSON representation
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[Base Config.py](https://github.com/deepmind/ferminet/blob/jax/ferminet/base_config.py)
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Machine Learning the Schrodinger Equation
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References
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ml_collections ConfigDict
- QUANTUM MONTE-CARLO INTEGRATION: THE FULL ADVANTAGE IN MINIMAL CIRCUIT DEPTH
- A MLIR Dialect for Quantum Assembly Languages
- Variational Principles in Quantum Monte Carlo: The Troubled Story of Variance Minimization
- Fermionic neural-network states for ab-initio electronic structure
- Data Driven Science & Engineering:Machine Learning, Dynamical Systems and Control
- Better, Faster Fermionic Neural Networks
- Quantum Entanglement in Deep Learning Architectures
- Solving Many-Electron Schrodinger Equation Using Deep Neural Networks
- Variational neural network ansatz for steady states in open quantum systems
- Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems
- Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- Optimizing Neural Networks with Kronecker-factored Approximate Curvature
- MontePython: Implementing Quantum Monte Carlo using Python
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- "Ab-Initio Solution of the Many-Electron Schroedinger Equation with Deep Neural Networks" - FermiNet
- Fermionic neural-network states for ab-initio electronic structure
- Quantum Entanglement in Deep Learning Architectures
- Solving Many-Electron Schrodinger Equation Using Deep Neural Networks
- Variational neural network ansatz for steady states in open quantum systems
- Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems
- QUANTUM MONTE-CARLO INTEGRATION: THE FULL ADVANTAGE IN MINIMAL CIRCUIT DEPTH
- Fermionic neural-network states for ab-initio electronic structure
- Data Driven Science & Engineering:Machine Learning, Dynamical Systems and Control
- Better, Faster Fermionic Neural Networks
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- MontePython: Implementing Quantum Monte Carlo using Python
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
- Fermionic neural-network states for ab-initio electronic structure
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[Setup.py](https://github.com/deepmind/ferminet/blob/jax/setup.py)
- Kfac - - [Kfac Paper](https://arxiv.org/pdf/1503.05671.pdf) :blue_book:
- ML Collections
- Kfac - - [Kfac Paper](https://arxiv.org/pdf/1503.05671.pdf) :blue_book:
- PyScf
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[Solving the Quantum Many-Body Problem with Artificial Neural Networks (2016)](https://arxiv.org/pdf/1606.02318.pdf)
- code version from the authors
- version of the paper in Jupyter Notebook
- many-body quantum state
- wavefunctions - _Optics_and_Modern_Physics_(OpenStax)/07%3A_Quantum_Mechanics/7.02%3A_Wavefunctions) of atoms and molecules using a [variational Monte Carlo](https://pubs.acs.org/doi/10.1021/acs.jctc.0c00147) approach.
- Hilbert space - made-simple-20160428/), as well as the typicality of a small number of physical states, are then the blocks on which modern approaches build upon to solve the many-body [Schrödinger’s equation](https://plus.maths.org/content/schrodinger-1) with a limited amount of classical resources.
- "Ab-Initio Solution of the Many-Electron Schroedinger Equation with Deep Neural Networks"
- version of the paper in code
- many-body quantum state
- Hilbert space - made-simple-20160428/), as well as the typicality of a small number of physical states, are then the blocks on which modern approaches build upon to solve the many-body [Schrödinger’s equation](https://plus.maths.org/content/schrodinger-1) with a limited amount of classical resources.
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Tools
- DeepQMC/PauliNet
- :bookmark_tabs: Publication - neural-network solution of the electronic Schrödinger equation
- FermiNet
- :bookmark_tabs: Publication - Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
- Hande
- :bookmark_tabs: Publication
- PySCF
- :bookmark_tabs: Publication
- :bookmark_tabs: Documentation
- PSi4
- :bookmark_tabs: Documentation
- QMCTorch
- NWChem
- :bookmark_tabs: Publication
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- :floppy_disk: Github
- :floppy_disk: : GitHub
- FermiNet
- :bookmark_tabs: Publication - neural-network solution of the electronic Schrödinger equation
- :floppy_disk: GitHub Code
- :bookmark_tabs: Publication
- :floppy_disk: Github
- :floppy_disk: mpi4pyscf - Message Passing Interface parallelism
- :floppy_disk: Github
- :bookmark_tabs: Publication
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- :floppy_disk: Github
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- PySCF
Categories
References
68
Tools
67
[Solving the Quantum Many-Body Problem with Artificial Neural Networks (2016)](https://arxiv.org/pdf/1606.02318.pdf)
9
[Base Config.py](https://github.com/deepmind/ferminet/blob/jax/ferminet/base_config.py)
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[Setup.py](https://github.com/deepmind/ferminet/blob/jax/setup.py)
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Machine Learning the Schrodinger Equation
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Sub Categories