Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys

Danial Khatamsaz; Brent Vela; Prashant Singh; D. D. Johnson; Douglas Allaire; Raymundo Arróyave

doi:10.1016/j.actamat.2022.118133

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Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys

Danial Khatamsaz Brent Vela Prashant Singh D. D. Johnson Douglas Allaire Raymundo Arróyave

Year: 2022 Journal: Acta Materialia Vol: 236 Pages: 118133-118133 Publisher: Elsevier BV

DOI: 10.1016/j.actamat.2022.118133

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Keywords:

Bayesian optimization Materials science Ductility (Earth science) Material Design Constraint (computer-aided design) Mathematical optimization Density functional theory Metric (unit) Computer science Mechanical engineering Composite material Mathematics Engineering

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Machine Learning in Materials Science

Physical Sciences → Materials Science → Materials Chemistry

Hydrogen embrittlement and corrosion behaviors in metals

Physical Sciences → Materials Science → Metals and Alloys

Titanium Alloys Microstructure and Properties

Physical Sciences → Materials Science → Materials Chemistry

Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys

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