论文标题
机器学习协助的交换相关功能的开发
Development of exchange-correlation functionals assisted by machine learning
论文作者
论文摘要
随着机器学习（ML）的最新快速进展，使用ML方法出现了一种新方法来交换密度功能理论的交换相关功能。在本章中，我们回顾了如何将ML工具用于此和最近实现的性能。据表明，ML不反对分析方法，对人类的直觉进行补充，并以期望的准确性将发展朝向第一原理计算。
With the recent rapid progress in the machine-learning (ML), there have emerged a new approach using the ML methods to the exchange-correlation functional of density functional theory. In this chapter, we review how the ML tools are used for this and the performances achieved recently. It is revealed that the ML, not being opposed to the analytical methods, complements the human intuition and advance the development toward the first-principles calculation with desired accuracy.
