Transferable atomic orbitals

This release contains all code required to reproduce results of our papers „Towards a transferable fermionic neural wavefunction for molecules“ and “Variational Monte Carlo on a Budget — Fine-tuning pre-trained Neural Wavefunctions“.

https://doi.org/10.1038/s41467-023-44216-9 (Nature Communications)
https://openreview.net/forum?id=FBNyccPfAu

Among other features it supports:

• Transferable atomic orbitals (TAO) to optimize a single neural network across molecules and geometrical conformations.
• Normal mode distortion for geometrical conformations to sample continously new geometrical conformations during shared optimization.
• A variation of PhisNet to generate orbital features for the TAO ansatz including a training framework.
• A dataset of molecules up to a size of four heavy atoms (Oxygen, Carbon, Nitrogen) for the pre-training of a TAO wavefunction.

While still contained in the codebase, some older features not relevant for the paper (such as weight-sharing or PauliNet-like architectures) have not been tested and might be broken in this release.