Predicting lattice thermal conductivity from fundamental material properties using machine learning techniques
Predicting lattice thermal conductivity from fundamental material properties using machine learning techniques
复制标题
使用机器学习技术根据基本材料特性预测晶格热导率
DOI：
10.1039/d2ta08721a
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发表时间：
2023
影响因子：
11.9
通讯作者：
Hu, Ming
中科院分区：
文献类型：
--
作者：
Qin, Guangzhao;Wei, Yi;Yu, Linfeng;Xu, Jinyuan;Ojih, Joshua;Rodriguez, Alejandro David;Wang, Huimin;Qin, Zhenzhen;Hu, Ming
高通量筛选和材料信息学在新材料的发现中显示出巨大的力量，包括电池、高熵合金和光催化剂。然而，基于晶格热导率（κ）的先进热材料高通量筛选仍局限于密集使用第一性原理计算，由于难以承受的计算成本要求，这不适用于快速、稳健和大规模的材料筛选。在这项研究中，利用15种机器学习算法从材料的基本物理和化学性质快速准确地预测κ。经过良好训练的模型成功地捕捉到了这些基本材料特性与不同类型材料的κ之间的内在相关性。此外，深度学习结合半监督技术显示出准确预测跨越4个数量级的不同κ值的能力，特别是对3716种新材料的外推预测能力。所开发的模型提供了一个强大的工具，大规模的先进的热功能材料筛选与目标的热传输性能。
High-throughput screening and material informatics have shown a great power in the discovery of novel materials, including batteries, high entropy alloys, and photocatalysts. However, the lattice thermal conductivity (κ) oriented high-throughput screening of advanced thermal materials is still limited to the intensive use of first principles calculations, which is inapplicable to fast, robust, and large-scale material screening due to the unbearable computational cost demanding. In this study, 15 machine learning algorithms are utilized for fast and accurate κ prediction from basic physical and chemical properties of materials. The well-trained models successfully capture the inherent correlation between these fundamental material properties and κ for different types of materials. Moreover, deep learning combined with a semi-supervised technique shows the capability of accurately predicting diverse κ values spanning 4 orders of magnitude, especially the power of extrapolative prediction on 3716 new materials. The developed models provide a powerful tool for large-scale advanced thermal functional materials screening with targeted thermal transport properties.