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 t...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 TiZrHfAlx.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.展开更多
基金The machine-learning studies were supported by ISIRD Phase-I grant(9-405/2019/IITRPR/3480)from IIT RoparThe work at Ames Laboratory,including theory developments for MPEAs,was supported by U.S.DOE Office of Science,Basic Energy Sciences,Materials Science&Engineering Division.Ames Laboratory is operated by ISU for the U.S.DOE under contract DE-AC02-07CH11358Experimental work and application of theory to this system was supported by the U.S.Department of Energy(DOE),Office of Fossil Energy,Crosscutting Research Program.The Advanced Photon Source use was supported by U.S.DOE,Office of Science,Office of Basic Energy Sciences under Contract No.DE-AC02-06CH11357.
文摘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 TiZrHfAlx.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.
