聚类分析
材料科学
合金
人工神经网络
理论(学习稳定性)
计算机科学
空位缺陷
形状记忆合金
机器学习
人工智能
算法
生物系统
结晶学
化学
冶金
生物
作者
Dishant Beniwal,Preeti Singh,Shivam Gupta,M. J. Kramer,D. D. Johnson,Pratik K. Ray
标识
DOI:10.1038/s41524-022-00842-3
摘要
Abstract Despite a plethora of data being generated on the mechanical behavior of multi-principal element alloys, a systematic assessment remains inaccessible via Edisonian approaches. We approach this challenge by considering the specific case of alloy hardness, and present a machine-learning framework that captures the essential physical features contributing to hardness and allows high-throughput exploration of multi-dimensional compositional space. The model, tested on diverse datasets, was used to explore and successfully predict hardness in Al x Ti y (CrFeNi) 1- x - y , Hf x Co y (CrFeNi) 1- x - y and Al x (TiZrHf) 1- x systems supported by data from density-functional theory predicted phase stability and ordering behavior. The experimental validation of hardness was done on TiZrHfAl x . The selected systems pose diverse challenges due to the presence of ordering and clustering pairs, as well as vacancy-stabilized novel structures. We also present a detailed model analysis that integrates local partial-dependencies with a compositional-stimulus and model-response study to derive material-specific insights from the decision-making process.
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