ANI-1 neural net potential with python interface (ASE)
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NOTICE: Python binaries built for python 3.6 and CUDA 9.2

Works only under Ubuntu variants of Linux with a NVIDIA GPU

This is a prototype interface for ANI-1x and ANI-1ccx neural network potentials for The Atomic Simulation Environment (ASE). Current ANI-1x and ANI-1ccx potentials provide predictions for the CHNO elements. The original ANI-1 and ANI-1x potentials are available in the "deprecated_original" original branch. For best performance the ANI-1x and ANI-1ccx ensembles in this branch should be used in any application.



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

To test the code run the python script: examples/

Computed energies from the quick test on a working installation are (eV):
Initial Energy: -2078.502822821320
Final Energy: -2078.504266011399

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

Cool stuff

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

MD simulation of Protein-ligand complex with deep learning potential ANI-1x

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

Nucleation of carbon nanoparticles from hot vapor simulation with ANI-1 deep learning potential

ANI-1 dataset

COMP6 benchmark


We now have a PyTorch implementation. See: Documents and GitHub


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 (2017), Article number: 170193, DOI: 10.1038/sdata.2017.193

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. The Journal of Chemical Physics 148, 241733 (2018), (

Active learning and transfer learning-based (ANI-1ccx):

Justin S. Smith, Benjamin T. Nebgen, Roman Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton Barros, Sergei Tretiak, Olexandr Isayev, Adrian Roitberg. Outsmarting Quantum Chemistry Through Transfer Learning. ChemRxiv, 2018, DOI: []