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.