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TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials

preprint
submitted on 29.04.2020 and posted on 04.05.2020 by Xiang Gao, Farhad Ramezanghorbani, Olexandr Isayev, Justin Smith, Adrian Roitberg
This paper presents TorchANI, a PyTorch based software for training/inference
of ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces and
other physical properties of molecular systems. ANI is an accurate neural network
potential originally implemented using C++/CUDA in a program called NeuroChem.
Compared with NeuroChem, TorchANI has a design emphasis on being light weight,
user friendly, cross platform, and easy to read and modify for fast prototyping, while
allowing acceptable sacrifice on running performance. Because the computation of
atomic environmental vectors (AEVs) and atomic neural networks are all implemented
using PyTorch operators, TorchANI is able to use PyTorch’s autograd engine to automatically compute analytical forces and Hessian matrices, as well as do force training
without additional codes required.

History

Email Address of Submitting Author

roitberg@ufl.edu

Institution

University of Florida

Country

USA

ORCID For Submitting Author

0000-0003-3963-8784

Declaration of Conflict of Interest

None

Exports