机器学习回归引导的热电材料发现综述

Guangshuai Han, Yixuan Sun, Yining Feng, Guang Lin, Na Lu
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引用次数: 20

摘要

热电材料由于具有将热能转化为电能的固态解决方案的潜力,近年来受到越来越多的关注。高性能的热电材料应具有高导电性和低导热性,两者通常呈正相关关系。这给寻找合适的候选人带来了挑战。设计热电材料通常需要以迭代的方式评估材料性能,这在实验和计算上都是昂贵的。机器学习由于其快速的推理时间而被认为是促进材料设计的有前途的工具。在本文中,我们总结了最近的进展,并介绍了机器学习应用于热电材料发现的整个工作流程,重点介绍了机器学习回归模型及其评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review
Thermoelectric materials have increasingly been given attention recently due to their potential of being a solid-state solution in converting heat energy to electricity. Good performing thermoelectric materials are expected to have high electrical conductivity and low thermal conductivity which are usually positively correlated. This poses a challenge in finding suitable candidates. Designing thermoelectric materials often requires evaluating material properties in an iterative manner, which is experimentally and computationally expensive. Machine learning has been regarded as a promising tool to facilitate material design thanks to its fast inference time. In this paper, we summarize recent progress and present the entire workflow in machine learning applications to thermoelectric material discovery, with an emphasis on machine learning regression models and their evaluation.
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