Neural Net Potential Energy Surface
Project description
pyKinML: Package for training Neural Net Potential Energy Surfaces
Description
This repository contains the code to train NNPESs and use those models with an ASE calculator.
How to install
This package can be installed with pip or by cloning this repo and installing it locally.
Install with pip:
pip install pykinml
Clone from repo:
git clone git@github.com:sandialabs/pykinml.git
This package relies on PyTorch_scatter to sum the atomic contributions to energy. Ensure you have the proper version. Instructions for installation can be found at: https://github.com/rusty1s/pytorch_scatter
We also highly recomend (required for force training) using the aevmod package for calculation of the aevs and their jacobians: https://github.com/sandialabs/aevmod.git
For transition state optimization, we recomend Sella: https://github.com/zadorlab/sella
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