ASE-ANI

NOTICE: Python binaries built for python 3.6 and CUDA 9

Works only under Ubuntu variants of Linux

This is a prototype interface for ANI-1x neural network potential for The Atomic Simulation Environment (ASE). Current ANI-1x potential implements CHNO elements.

##REQUIREMENTS:

• Python 3.6 (we recommend Anaconda distribution)
• Modern NVIDIA GPU, compute capability 5.0 of newer.
• CUDA 9.0
• ASE
• Modified ased3 for D3 van der Waals correction (Optional)
• MOPAC2012 or MOPAC2016 for some examples to compare results (Optional)

Installation

Clone this repository into desired folder and add environmental variables from bashrc_example.sh to your .bashrc.

For use cases please refer to examples folder with several iPython notebooks

Cool stuff

Teaser of the new ANI-2x (CHNOSFCl) potential in action!

ANI-1x running 5ns MD on a box of C₂ at high temperature.

ANI-1 dataset

https://github.com/isayev/ANI1_dataset

COMP6 benchmark

https://github.com/isayev/COMP6

Citation

If you use this code, please cite:

ANAKIN-ME ML Potential Method:

Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg. ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost. Chemical Science, 2017, DOI: 10.1039/C6SC05720A

Original ANI-1 data:

Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg. ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules. Scientific Data, 4, Article number: 170193, DOI: 10.1038/sdata.2017.193 https://www.nature.com/articles/sdata2017193

Active-learning based (ANI-1x):

Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E. Roitberg. Less is more: sampling chemical space with active learning. arXiv, 2018, DOI: [arXiv:1801.09319] (https://arxiv.org/abs/1801.09319)