https://github.com/cgtbatista/ase_ani

ANI-1 neural net potential with python interface (ASE)

https://github.com/cgtbatista/ase_ani

Repository

ANI-1 neural net potential with python interface (ASE)

https://github.com/cgtbatista/ASE_ANI/blob/master/

# ASE-ANI

### 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.

## REQUIREMENTS:
* Python 3.6 (we recommend [Anaconda](https://www.continuum.io/downloads) distribution)
* Modern NVIDIA GPU, [compute capability 5.0](https://developer.nvidia.com/cuda-gpus) of newer.
* [CUDA 9.2](https://developer.nvidia.com/cuda-downloads)
* [ASE](https://wiki.fysik.dtu.dk/ase/index.html)
* 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`. 
 

To test the code run the python script: examples/ani_quicktest.py


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! 


### 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

## TorchANI

We now have a PyTorch implementation. See: [Documents](https://aiqm.github.io/torchani/) and [GitHub](https://github.com/aiqm/torchani)

## 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](http://pubs.rsc.org/en/content/articlelanding/2017/sc/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 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*. The Journal of Chemical Physics 148, 241733 (2018), (https://aip.scitation.org/doi/abs/10.1063/1.5023802)

### 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: [https://doi.org/10.26434/chemrxiv.6744440.v1]

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