This paper presents TorchANI, a PyTorch-based program 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 lightweight, 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 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 requiring any additional codes. TorchANI is open-source and freely available on GitHub: https://github.com/aiqm/torchani.

中文翻译：

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TorchANI：基于PyTorch的免费和开放源代码的ANI神经网络潜力深度学习实现。

本文介绍了TorchANI，这是一个基于PyTorch的程序，用于训练/推理ANI（ANAKIN-ME）深度学习模型，以获得势能面和分子系统的其他物理特性。ANI是一种准确的神经网络潜力，最初是在称为NeuroChem的程序中使用C ++ / CUDA实现的。与NeuroChem相比，TorchANI的设计重点是轻巧，用户友好，跨平台，易于阅读和修改以实现快速原型制作，同时允许牺牲运行性能。由于原子环境矢量和原子神经网络的计算全部使用PyTorch运算符实现，因此TorchANI能够使用PyTorch的autograd引擎自动计算分析力和Hessian矩阵，并且无需任何其他代码即可进行力训练。