Distilling physical origins of hardness in multi-principal element alloys directly from ensemble neural network models

Beniwal, D.; Singh, P.; Gupta, S.; Kramer, M. J.; Johnson, D. D.; Ray, P. K.

doi:10.1038/s41524-022-00842-3

Title: Distilling physical origins of hardness in multi-principal element alloys directly from ensemble neural network models

Journal Article · 17 July 2022 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-022-00842-3 · OSTI ID:1876511

; Singh, P.; Gupta, S.;

; Johnson, D. D.;

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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Research Organization:: Ames Laboratory (AMES), Ames, IA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Fossil Energy (FE); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1876511

Report Number(s):: IS-J 10,851; 153; PII: 842

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 8; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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