Open-Source Fermionic Neural Networks with Ionic Charge Initialization
Abstract: Finding accurate solutions to the electronic Schr\"odinger equation plays an important role in discovering important molecular and material energies and characteristics. Consequently, solving systems with large numbers of electrons has become increasingly important. Variational Monte Carlo (VMC) methods, especially those approximated through deep neural networks, are promising in this regard. In this paper, we aim to integrate one such model called the FermiNet, a post-Hartree-Fock (HF) Deep Neural Network (DNN) model, into a standard and widely used open source library, DeepChem. We also propose novel initialization techniques to overcome the difficulties associated with the assignment of excess or lack of electrons for ions.
- Fast-flows: Flow-based models for molecular graph generation. https://arxiv.org/abs/2201.12419. arXiv:2201.12419.
- Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity. arXiv:1703.10603.
- Horace He, R. Z. 2021. functorch: JAX-like composable function transforms for PyTorch. https://github.com/pytorch/functorch.
- FermiNet.
- Mulliken, R. S. 1955. Electronic population analysis on LCAO–Mo molecular wave functions. I. The Journal of Chemical Physics, 23(10): 1833–1840.
- National Institute of Standards and Technology. n.d. Computational Chemistry Comparison and Benchmark DataBase (CCCBDB).
- Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks. Phys. Rev. Research, 2: 033429.
- Making DeepChem a Better Framework for AI-Driven Science.
- Differentiable Physics: A Position Piece.
- DeepQMC: An open-source software suite for variational optimization of deep-learning molecular wave functions. The Journal of Chemical Physics, 159(9): 094108.
- Better, Faster Fermionic Neural Networks. arXiv:2011.07125.
- Recent developments in the PySCF program package. The Journal of Chemical Physics, 153(2).
- Open Source Infrastructure for Differentiable Density Functional Theory. In 1st Workshop on the Synergy of Scientific and Machine Learning Modeling @ ICML2023.
- MoleculeNet: A Benchmark for Molecular Machine Learning. Chem. Sci., 9: 513–530.
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