High-throughput calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi-principal element alloys

Chengchen Jin, Kai Xiong, Congtao Luo, Hui Fang, Chaoguang Pu, Hua Dai, Aimin Zhang, Shunmeng Zhang, Yingwu Wang
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Abstract

The traditional trial-and-error method for designing refractory multi-principal element alloys (RMPEAs) is inefficient due to a vast compositional design space and high experimental costs. To surmount this challenge, the data-driven material design based on machine learning (ML) has emerged as a critical tool for accelerating materials design. However, the absence of robust datasets impedes the exploitation of machine learning in designing novel RMPEAs. High-throughput (HTP) calculations have enabled the creation of such datasets. This study addresses these challenges by developing a data-driven framework for predicting the elastic properties of RMPEAs, integrating HTP calculations with ML. A big dataset of RMPEAs including 4536 compositions was constructed using the new proposed HTP method. A novel stacking ensemble regression algorithm combining multilayer perceptron (MLP) and gradient boosting decision tree (GBDT) was developed, which achieved 92.9% accuracy in predicting the elastic properties of Ti-V-Nb-Ta alloys. Verification experiments confirmed the ML model's accuracy and robustness. This integration of HTP calculations and ML provides a cost-effective, efficient, and precise alloy design strategy, advancing RMPEAs development.

Abstract Image

结合堆垛集成机器学习的高通量计算方法预测难熔多主元合金的弹性性能
传统的试错法设计难熔多主元素合金(rmpea),由于成分设计空间大,实验成本高,效率低。为了克服这一挑战,基于机器学习(ML)的数据驱动材料设计已经成为加速材料设计的关键工具。然而,缺乏健壮的数据集阻碍了机器学习在设计新型rmpea中的应用。高吞吐量(HTP)计算使得创建这样的数据集成为可能。本研究通过开发一个数据驱动框架来预测rmpea的弹性特性,将HTP计算与ML相结合,从而解决了这些挑战。使用新提出的HTP方法构建了包含4536种成分的rmpea大数据集。提出了一种多层感知器(MLP)和梯度增强决策树(GBDT)相结合的叠加集成回归算法,对Ti-V-Nb-Ta合金弹性性能的预测准确率达到92.9%。验证实验验证了该模型的准确性和鲁棒性。这种HTP计算和ML的集成提供了一种具有成本效益,高效和精确的合金设计策略,推进了rmpea的开发。
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