NeuralXC

Implementation of a machine learned density functional as presented in Machine learning accurate exchange and correlation functionals of the electronic density. Nat Commun 11, 3509 (2020)

NeuralXC only includes routines to fit and test neural network based density functionals. To use these functionals for self-consistent calculations within electronic structure codes please refer to Libnxc.

The basic premise of NeuralXC can be summarized as follows:

1. The electron density on a real space grid is projected onto a set of atom-centered atomic orbitals
2. The projection coefficients are symmetrized to ensure that systems that only differ by a global rotation have the same energy
3. The symmetrized coefficients are fed through a neural network architecture that is invariant under atom permutations, similar to Behler-Parrinello networks.
4. The output of this neural network is the exchange-correlation (XC) energy (correction) for a given system. The XC-potential is then obtained by backpropagating through steps 1-3.

The very nature of this approach lends itself to a modular implementation. Hence, we have separated NeuralXC into three main modules, Projector, Symmetrizer, and ML, each of which can be individually customized.

Contents:

• Installation
• Quickstart
• Filetypes
• Self-consistent training
• CLI
  • Data
  • Model
  • Other
• Projector
  • Base classes and inheritance
  • Radial basis
  • PySCF projector
• Symmetrizer
• Configuration Files
  • config.json
  • hyper.json

Indices and tables

• Index
• Module Index
• Search Page