arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.05831cond-mat.mtrl-sciphysics.data-an
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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

Kazuki Morita, Daniel W. Davies, Keith T. Butler, Aron Walsh

摘要:

晶体的相对介电常数是将微观化学键合与宏观电磁响应联系起来的基本性质。多个模型,包括分析,数值和统计的描述,已经了解和预测介电行为。分析模型通常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,我们结合监督ML,密度泛函微扰理论和基于博弈论的分析来预测和解释晶体的光学介电常数的物理趋势。两个ML模型,支持向量回归和深度神经网络,在1,364个介电常数的数据集上进行了训练。对ML模型的Shapley加法解释(SHAP)分析表明,它们恢复了教科书Clausius-Mossotti和Penn模型描述的相关性,这使人们对它们描述物理行为的能力充满信心,同时提供了卓越的预测能力。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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