论文标题
使用机器学习对晶体的介电常数进行建模
Modelling the dielectric constants of crystals using machine learning
论文作者
论文摘要
晶体的相对介电常数是将微观化学键合与宏观电磁反应联系起来的基本特性。已经制定了多种模型，包括分析，数值和统计描述，以理解和预测介电行为。分析模型通常仅限于特定类型的化合物，而机器学习（ML）模型通常缺乏可解释性。在这里，我们结合了受监督的ML，密度功能扰动理论和基于游戏理论的分析，以预测和解释晶体光学介电常数的物理趋势。在1,364个介电常数的数据集上对两个ML模型（支持向量回归和深神网络）进行了培训。 Shapley对ML模型的添加说明（SHAP）分析表明，他们恢复了教科书Clausius-Mossotti和Penn模型所描述的相关性，这使他们对描述身体行为的能力有信心，同时提供了出色的预测能力。
The relative permittivity of a crystal is a fundamental property that links microscopic chemical bonding to macroscopic electromagnetic response. Multiple models, including analytical, numerical and statistical descriptions, have been made to understand and predict dielectric behaviour. Analytical models are often limited to a particular type of compounds, whereas machine learning (ML) models often lack interpretability. Here, we combine supervised ML, density functional perturbation theory, and analysis based on game theory to predict and explain the physical trends in optical dielectric constants of crystals. Two ML models, support vector regression and deep neural networks, were trained on a dataset of 1,364 dielectric constants. Shapley additive explanations (SHAP) analysis of the ML models reveals that they recover correlations described by textbook Clausius-Mossotti and Penn models, which gives confidence in their ability to describe physical behavior, while providing superior predictive power.
