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

This paper presents TorchANI, a PyTorch based software for training/inferenceof ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces andother physical properties of molecular systems. ANI is an accurate neural networkpotential 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, whileallowing acceptable sacrifice on running performance. Because the computation ofatomic environmental vectors (AEVs) and atomic neural networks are all implementedusing PyTorch operators, TorchANI is able to use PyTorch’s autograd engine to automatically compute analytical forces and Hessian matrices, as well as do force trainingwithout additional codes required.