论文标题
机器学习和晶体轨道汉密尔顿人口的金属眼镜中的化学键合
Chemical Bonding in Metallic Glasses from Machine Learning and Crystal Orbital Hamilton Population
论文作者
论文摘要
金属玻璃（MGS）的化学（组成和键合信息）至少与结构拓扑相同，以了解其特性和生产/加工特殊性。本文报告了一种基于机器学习（ML）的方法，该方法带来了原型合金系统MG中史无前例的“大图”观点。电子结构与化学键合之间的联系由晶体轨道汉密尔顿人口（COHP）分析给出。在密度功能理论（DFT）的框架内。通过：通过COHP分析提供的债券强度的有效定量估计，可以使所陈述的综合概述成为可能；关于结构的代表性统计数据，就经典分子动力学模拟实现的原子构型而言；以及原子位置（SOAP）描述符的平滑重叠。该研究是通过在机械载荷范围下应用该ML模型的应用来补充的。在化学键强度的产生概述中，揭示了化学/结构异质性，这符合对原子局部环境验证的键交换趋势。令人鼓舞的结果铺平了朝着适用于许多其他环境中的替代方法的道路，在许多其他情况下，原子分类（从化学债券的角度）起着关键作用。
The chemistry (composition and bonding information) of metallic glasses (MGs) is at least as important as structural topology for understanding their properties and production/processing peculiarities. This article reports a machine learning (ML)-based approach that brings an unprecedented "big picture" view of chemical bond strengths in MGs of a prototypical alloy system. The connection between electronic structure and chemical bonding is given by crystal orbital Hamilton population (COHP) analysis; within the framework of density functional theory (DFT). The stated comprehensive overview is made possible through a combination of: efficient quantitative estimate of bond strengths supplied by COHP analysis; representative statistics regarding structure in terms of atomic configurations achieved with classical molecular dynamics simulations; and the smooth overlap of atomic positions (SOAP) descriptor. The study is supplemented by an application of that ML model under the scope of mechanical loading; in which the resulting overview of chemical bond strengths revealed a chemical/structural heterogeneity that is in line with the tendency to bond exchange verified for atomic local environments. The encouraging results pave the way towards alternative approaches applicable in plenty of other contexts in which atom categorization (from the perspective of chemical bonds) plays a key role.
