# Open-Source Fermionic Neural Networks with Ionic Charge Initialization

arXiv: https://arxiv.org/abs/2401.10287 · HTML: https://mlsanity.com/paper/2401.10287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networks](https://mlsanity.com/paper/1909.02487/) (2019)
- [Better, Faster Fermionic Neural Networks](https://mlsanity.com/paper/2011.07125/) (2020)
- [Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity](https://mlsanity.com/paper/1703.10603/) (2017)
- [Differentiable Physics: A Position Piece](https://mlsanity.com/paper/2109.07573/) (2021)

## Abstract (arXiv)

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.