论文标题
来自多保真数据的有序和无序材料的学习特性
Learning Properties of Ordered and Disordered Materials from Multi-fidelity Data
论文作者
论文摘要
从其原子的布置中预测材料的特性是材料科学的基本目标。尽管近年来，机器学习已成为一种新的范式，可以快速地预测材料特性，但其实际效用受到高保真数据的稀缺的限制。在这里，我们开发了多保真图网络，作为一种通用方法，可以实现具有小数据大小的材料属性的准确预测。作为概念的证明，我们表明，低保真性perdew-burke-ernzerhof带隙大大增强了材料图中潜在结构特征的分辨率，从而导致实验带隙预测的平均绝对错误降低了22-45 \％。我们进一步证明，在材料图网络中学习的元素嵌入为材料中的模型障碍提供了一种自然方法，从而解决了材料属性计算预测中的基本差距。
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.
