Open-Source Fermionic Neural Networks with Ionic Charge Initialization
This work provides an incremental advancement in quantum chemistry simulations by making a specialized method more accessible and addressing initialization challenges for ions.
The authors integrated the FermiNet, a deep neural network model for solving the electronic Schrödinger equation, into the open-source library DeepChem and proposed novel initialization techniques to handle ionic charge issues, achieving unspecified improvements in computational efficiency.
Finding accurate solutions to the electronic Schrödinger 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.