Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
J Chem Phys. 2020 Feb 7;152(5):050902. doi: 10.1063/1.5126336.
The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler published a perspective in this journal providing an overview of some of the leading methods in this field. In this perspective, we provide an updated discussion of recent developments, emerging trends, and promising areas for future research in this field. We include in this discussion an overview of three emerging approaches to developing machine-learned interatomic potential models that have not been extensively discussed in existing reviews: moment tensor potentials, message-passing networks, and symbolic regression.
使用监督机器学习来开发快速准确的原子间势模型，通过大大加快原子尺度模拟而几乎不损失准确性，从而改变了分子和材料研究。三年前，Jörg Behler 在本期刊上发表了一篇观点文章，概述了该领域的一些主要方法。在这篇观点文章中，我们提供了一个关于该领域最新发展、新兴趋势和未来研究有希望领域的更新讨论。我们在讨论中包括了对三种新兴的机器学习原子间势模型开发方法的概述，这些方法在现有的综述中没有得到广泛讨论：张量势、消息传递网络和符号回归。
