Owing to their simultaneous accuracy and computational efficiency, interatomic potentials machine-learned using first-principles calculation data are promising for investigating phenomena closely related to atomic motion in various energy materials. We have been working with one type of these potentials, high-dimensional (HD) neural network potentials (NNPs), and their applications, but we realized that our current understanding of HD NNPs, e.g. the meaning of the atomic energy mapping, remained insufficient, and that tuning their prediction performance for different target properties/phenomena often requires much trial and error. In this article, we illustrate the usefulness of NNPs through our studies on ion migration and thermal transport in energy and related materials. We also share our experiences with data sampling and training strategies and discuss the meaning of atomic energy mapping in HD NNPs.

由于其同时的准确性和计算效率，使用第一性原理计算数据机器学习的原子间电势有望用于研究与各种能量材料中的原子运动密切相关的现象。我们一直在研究其中一种类型的电势，即高维（HD）神经网络电势（NNP）及其应用，但我们意识到，目前对HD NNP的理解（例如原子能图的含义）仍然不足，并且针对不同的目标属性/现象调整其预测性能通常需要大量的反复试验。在本文中，我们通过对能量和相关材料中离子迁移和热传输的研究来说明NNP的有用性。