Zhang Yaolong, Hu Ce, Jiang Bin
Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China.
J Phys Chem Lett. 2019 Sep 5;10(17):4962-4967. doi: 10.1021/acs.jpclett.9b02037. Epub 2019 Aug 14.
We propose a simple, but efficient and accurate, machine learning (ML) model for developing a high-dimensional potential energy surface. This so-called embedded atom neural network (EANN) approach is inspired by the well-known empirical embedded atom method (EAM) model used in the condensed phase. It simply replaces the scalar embedded atom density in EAM with a Gaussian-type orbital based density vector and represents the complex relationship between the embedded density vector and atomic energy by neural networks. We demonstrate that the EANN approach is equally accurate as several established ML models in representing both big molecular and extended periodic systems, yet with much fewer parameters and configurations. It is highly efficient as it implicitly contains the three-body information without an explicit sum of the conventional costly angular descriptors. With high accuracy and efficiency, EANN potentials can vastly accelerate molecular dynamics and spectroscopic simulations in complex systems at ab initio level.
我们提出了一种简单但高效且准确的机器学习（ML）模型，用于构建高维势能面。这种所谓的嵌入原子神经网络（EANN）方法的灵感来源于凝聚相中著名的经验嵌入原子方法（EAM）模型。它只是用基于高斯型轨道的密度向量取代了EAM中的标量嵌入原子密度，并通过神经网络表示嵌入密度向量与原子能量之间的复杂关系。我们证明，在表示大分子和扩展周期系统方面，EANN方法与几个已确立的ML模型具有同等的准确性，但所需的参数和配置要少得多。它非常高效，因为它隐含地包含三体信息，而无需显式求和传统的昂贵角度描述符。凭借高精度和高效率，EANN势能可以在从头算水平上极大地加速复杂系统中的分子动力学和光谱模拟。
