Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface,Journal of Chemical Theory and Computation

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Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2023-10-19 , DOI: 10.1021/acs.jctc.3c00873
Pei-Lin Kang ₁ , Zheng-Xin Yang ₁ , Cheng Shang ₁ , Zhi-Pan Liu _{1,

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Affiliation

The high dimensional machine learning potential (MLP) that has developed rapidly in the past decade represents a giant step forward in large-scale atomic simulation for complex systems. The long-range interaction and the poor description of chemical reactions are typical problems of high dimensional MLP, which are mainly caused by the poor structure discrimination of the atom-centered ML model. Herein, we propose a low-cost neural-network-based MLP architecture for fitting global potential energy surface data, namely, G-MBNN, that can offer improved energy and force resolution on a complex potential energy surface. In G-MBNN, a set of many-body energy terms based on the local atomic environment are explicitly included in computing the total energy─the total energy of the system is written as the sum of atomic energy and many-body energy contributions. These extra many-body energy terms are computationally low-cost and, importantly, can provide easy access to delicate energy terms in complex systems such as very short repulsion, long-range attractions, and sensitive angular-dependent covalent interactions. We implement G-MBNN in the LASP code and demonstrate the improved accuracy of the new framework in representative systems, including ternary-element energy materials LiCoO_x, TiO₂ with defects, and a series of organic reactions.

中文翻译：

具有显式多体函数的全局神经网络势，用于改进复杂势能面的描述

高维机器学习潜力（MLP）在过去十年中迅速发展，代表着复杂系统大规模原子模拟的巨大进步。长程相互作用和化学反应描述能力差是高维MLP的典型问题，这主要是由原子中心ML模型的结构辨别能力差造成的。在此，我们提出了一种基于低成本神经网络的 MLP 架构，用于拟合全局势能表面数据，即 G-MBNN，它可以在复杂势能表面上提供改进的能量和力分辨率。在G-MBNN中，在计算总能量时明确包含一组基于局部原子环境的多体能量项——系统的总能量被写为原子能量和多体能量贡献之和。这些额外的多体能量项的计算成本较低，而且重要的是，可以轻松访问复杂系统中的微妙能量项，例如非常短的排斥力、长程吸引力和敏感的角度依赖性共价相互作用。我们在LASP代码中实现了G-MBNN，并在三元能源材料LiCoO _x、有缺陷的TiO ₂和一系列有机反应等代表性系统中证明了新框架的准确性的提高。

更新日期：2023-10-19

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