arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.04338cond-mat.mtrl-scicond-mat.dis-nn
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从多保真度数据中学习有序和无序材料的性质

Learning Properties of Ordered and Disordered Materials from Multi-fidelity Data

Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li, Shyue Ping Ong

摘要:

从原子的排列来预测材料的性质是材料科学的一个基本目标。虽然机器学习近年来已经成为一种新的范式,可以快速预测材料的性能,但由于缺乏高保真数据,它们的实际效用受到限制。在这里,我们开发了多保真度图网络作为一种通用的方法,以实现小数据量的材料性能的准确预测。作为一个概念的证明,我们表明,包含低保真度Perdew-Burke-Ernzerhof带隙极大地提高了材料图中的潜在结构特征的分辨率,导致实验带隙预测的平均绝对误差减少22-45\%。我们进一步证明了材料图网络中的学习元素嵌入提供了一种自然的方法来模拟材料中的无序,解决了材料特性计算预测中的根本差距。

英文摘要:

Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a new paradigm to provide rapid predictions of materials properties, their practical utility is limited by the scarcity of high-fidelity data. Here, we develop multi-fidelity graph networks as a universal approach to achieve accurate predictions of materials properties with small data sizes. As a proof of concept, we show that the inclusion of low-fidelity Perdew-Burke-Ernzerhof band gaps greatly enhances the resolution of latent structural features in materials graphs, leading to a 22-45\% decrease in the mean absolute errors of experimental band gap predictions. We further demonstrate that learned elemental embeddings in materials graph networks provide a natural approach to model disorder in materials, addressing a fundamental gap in the computational prediction of materials properties.

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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