npj: 载流子弛豫时间—机器学习高通量预测

来源：知社学术圈

近年来，被称为科学研究范式“第四次工业革命”的大数据与人工智能的结合引起了研究者们强烈的关注。其中，作为核心技术之一的机器学习已被成功地应用于物理学、材料科学、生物医药、图像识别等诸多领域。特别地，机器学习在新能源材料的探索上也在以惊人的速度发展。其中，对于高性能热电材料的快速搜寻尤为引人关注。在与热电性能密切相关的输运系数中，载流子弛豫时间的预测一直以来都是一个十分重要但又较为复杂的基础科学问题。虽然人们可以使用简单的形变势理论或是全面考虑电-声耦合来预测弛豫时间，但这两种方法都只能处理原胞较小的材料体系。

来自武汉大学物理科学与技术学院的刘惠军教授团队，采用一种名为SISSO（确定独立筛选与稀疏操作符）的机器学习方法，提出了形式简单并且物理意义明确的高通量描述符，快速有效地预测了超过1600万个具有任意化学配比的辉碲铋矿族化合物的载流子弛豫时间。这项研究工作对于高性能热电材料的搜寻以及层状拓扑材料热电性能的优化，具有重要的指导意义。

该文近期发表于npj Computational Materials 6: 149 (2020)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

High-throughput prediction of the carrier relaxation time via data-driven descriptor

Zizhen Zhou, Guohua Cao, Jianghui Liu, Huijun Liu

It has been demonstrated that many promising thermoelectric materials such as tetradymite compounds are also three-dimensional topological insulators. In both cases, a fundamental question is the evaluation of carrier relaxation time, which is usually a rough task due to the complicated scattering mechanisms. Previous works using the simple deformation potential theory or considering complete electron-phonon coupling are however restricted to small systems. By adopting a data-driven method named SISSO (Sure Independence Screening and Sparsifying Operator) with the training data obtained via deformation potential theory, we propose an efficient and physically interpretable descriptor to evaluate the relaxation time, using tetradymites as prototypical examples. Without any input from first-principles calculations, the descriptor contains only several elemental properties of the constituent atoms, and could be utilized to quickly and reliably predict the carrier relaxation time of a substantial number of tetradymites with arbitrary stoichiometry.