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基于机器学习的形成能量预测和IrRuRhMoW高合金的组合设计

Yihao Zheng¹, Xiangcui Qiu¹, Haibo Li¹

¹Shandong Provincial Key Laboratory/Collaborative Innovation Center of Chemical Energy Storage & Novel Cell Technology, School of Chemistry and Chemical Engineering, Liaocheng University, Liaocheng 252000, China.

Langmuir : the ACS journal of surfaces and colloids

|August 8, 2025

概括

机器学习 (ML) 与密度函数理论 (DFT) 结合,可以有效地预测高合金 (HEA) 的形成能量. 回归实现了最佳准确性,识别了优化IrRuRhMoW合金稳定性的关键元素.

科学领域:

材料科学材料科学材料科学
计算材料科学科学计算材料科学
热力学是一种热力学.

背景情况:

形成能量对于评估高合金 (HEA) 的热力学稳定性至关重要.
准确预测形成能量指导合金组成设计和性能优化.
传统的方法可能是计算密集型,需要高效的预测框架.

研究的目的:

开发和验证一种高效的机器学习 (ML) 框架,用于预测IrRuRhMoW高合金 (HEA) 的形成能量.
确定影响这些合金形成能量的关键描述因素和元素贡献.
探索构成空间以优化HEA稳定性.

主要方法:

集成密度函数理论 (DFT) 计算与机器学习 (ML) 模型.
利用特殊的准随机结构 (SQS) 方法生成面中心立方超细胞模型 (108个原子).
开发了一个描述器系统 (14个特征) 并评估了四个ML算法 (回归,随机森林,XGBoost,ANN),采用一步向前特征选择.

主要成果:

斜坡回归在评估的ML模型中显示出优越的预测准确性,稳定性和概括性.
影响形成能量的关键特征包括混合度,原子半径偏差,平均原子质量以及Ru和Ir的分数.
一个简化的7个特征模型在一个独立的测试套件上实现了高精度 (MAE为0.00562 ± 0.00007 eV/原子).
组成趋势表明Mo和W有利于较低的形成能量,而Ru和Rh增加了它.

结论:

开发的ML-DFT框架有效地预测了IrRuRhMoW HEAs的形成能量.
斜坡回归是这个预测任务的一个非常有效的模型.
对元素贡献的洞察力为优化合金组成和热力学稳定性提供了指导.

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