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arXiv 2602.01149cond-mat.mtrl-scics.LG
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鲁棒机器学习框架用于高性能半Heusler热电材料的可靠发现

A Generalizable Machine Learning Framework for Half-Heusler Thermoelectrics

ICGM, Univ Montpellier, CNRS, ENSCM(ICGM,蒙彼利埃大学,国家科学研究中心,ENSCM)

Shoeb Athar, Adrien Mecibah, Philippe Jund

AI总结:

本研究提出鲁棒机器学习框架,通过PCA分割、贝叶斯优化和SISSO分析,提升热电材料zT预测的泛化能力,发现新型高性能半Heusler热电材料。

摘要:

机器学习(ML)可以促进高效热电(TE)材料发现,这对于解决环境危机至关重要。然而,尽管ML模型的指标较高,但往往在实验泛化性方面表现不佳。本研究提出了一种稳健的工作流程,应用于半Heusler(hH)结构原型,用于图示指标(zT)预测,以提高ML模型的泛化能力。为了解决数据集处理和特征过滤的挑战,我们首先引入了一种严格的基于PCA的分割方法,确保训练集和测试集无偏且能代表完整的化学空间。然后,我们整合贝叶斯超参数优化与k-best特征过滤,跨三种架构——随机森林、XGBoost和神经网络——同时采用SISSO符号回归进行物理洞察和比较。利用SHAP和SISSO分析,我们确定A位掺杂浓度(xA')和A位汽化热(HVA)是除了温度(T)之外的主要zT驱动因素。最后,对约6.6×10^8种潜在化合物进行高通量筛选,通过稳定性约束过滤后,得到了几个新型高zT候选材料。突破传统关注改进模型的测试RMSE/R²值,本研究将关注点转移到建立测试集作为模型泛化能力的真实代理,并加强现有ML工作流程中常被忽视的模块,以促进数据驱动的下一代热电材料设计。

英文摘要:

Machine learning (ML) has emerged as a powerful approach for accelerating the discovery of thermoelectric (TE) materials, yet many models suffer from limited generalizability. Here we present a robust ML framework for predicting the thermoelectric figure of merit (zT) in half-Heusler (hH) compounds, emphasizing prediction of previously unseen compositions. A principal component analysis (PCA)-based train/test splitting strategy is introduced to generate representative partitions of chemical space. Bayesian hyperparameter optimization is combined with correlation-based k-best feature selection for Random Forest, XGBoost, and Neural Network models, together with the interpretable SISSO symbolic-regression method. Relative to the full set of 118 input features, the optimized feature subsets reduce the test RMSE by 16-27%. SHAP and SISSO analyses identify temperature, A-site dopant concentration, and A-site heat of vaporization as the most important descriptors of zT. The framework is validated through prediction of previously unexplored compositions, recovery of literature-reported optimal zT values for established hH systems, and first-principles calculations for the new compounds CeCoBi and CeIrBi, which exhibit favorable electronic transport properties. The framework is further applied to high-throughput screening of the doped hH chemical space, covering about 2.46 million elemental substitution combinations across 631 thermodynamically screened parent matrices under stability constraints. This screening yields a concise set of promising candidates for future study. Overall, the work establishes a generalizable workflow for reliable data-driven thermoelectric materials discovery.

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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