论文标题
通过机器学习方法检测简单共价系统的电子结构中的非本地效应
Detecting non-local effects in the electronic structure of a simple covalent system with machine learning methods
论文作者
论文摘要
使用机器学习中借来的方法，我们以一种简单的碳原子共价系统中对局部物理特性的远程远程效果进行了完全算法的远程检测。许多配置都存在这些远距离效应的事实意味着，基于局部假设的原子模拟方法（例如力场或现代机器学习方案）的准确性受到限制。我们表明，远距离效应的基本驾驶机制是电荷转移。如果已知电荷转移，则可以以一定量（例如带结构能量）的一定数量回收局部性。
Using methods borrowed from machine learning we detect in a fully algorithmic way long range effects on local physical properties in a simple covalent system of carbon atoms. The fact that these long range effects exist for many configurations implies that atomistic simulation methods, such as force fields or modern machine learning schemes, that are based on locality assumptions, are limited in accuracy. We show that the basic driving mechanism for the long range effects is charge transfer. If the charge transfer is known, locality can be recovered for certain quantities such as the band structure energy.
