https://github.com/mdsunivie/deeperwin

DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions to the multi-electron Schrödinger equation. DeepErwin supports weight-sharing when optimizing wave functions for multiple nuclear geometries and the usage of pre-trained neural network weights to accelerate optimization.
https://github.com/mdsunivie/deeperwin

deep-learning deep-neural-networks physical-chem quantum-monte-carlo schrodinger-equation transfer-learning variational-monte-carlo weight-sharing

Last synced: 2 months ago
JSON representation

DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions to the multi-electron Schrödinger equation. DeepErwin supports weight-sharing when optimizing wave functions for multiple nuclear geometries and the usage of pre-trained neural network weights to accelerate optimization.

• Host: GitHub
• URL: https://github.com/mdsunivie/deeperwin
• Owner: mdsunivie
• License: other
• Created: 2021-06-14T15:18:32.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2024-06-07T15:52:47.000Z (8 months ago)
• Last Synced: 2024-08-11T09:55:17.219Z (5 months ago)
• Topics: deep-learning, deep-neural-networks, physical-chem, quantum-monte-carlo, schrodinger-equation, transfer-learning, variational-monte-carlo, weight-sharing
• Language: Python
• Homepage:
• Size: 8.21 MB
• Stars: 46
• Watchers: 3
• Forks: 6
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• best-of-atomistic-machine-learning - GitHub