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Machine learning (ML) is shown to predict new alloys and their performancesin a high dimensional, multiple-target-property design space that considerschemistry, multi-step processing routes, and characterization methodologyvariations. A physics-informed featured engineering approach is shown to enableotherwise poorly performing ML models to perform well with the same data.Specifically, previously engineered elemental features based on alloychemistries are combined with newly engineered heat treatment process features.The new features result from first transforming the heat treatment parameterdata as it was previously recorded using nonlinear mathematical relationshipsknown to describe the thermodynamics and kinetics of phase transformations inalloys. The ability of the ML model to be used for predictive design isvalidated using blind predictions. Composition - process - propertyrelationships for thermal hysteresis of shape memory alloys (SMAs) with complexmicrostructures created via multiplemelting-homogenization-solutionization-precipitation processing stagevariations are captured, in addition to the mean transformation temperatures ofthe SMAs. The quantitative models of hysteresis exhibited by such highlyprocessed alloys demonstrate the ability for ML models to design for physicalcomplexities that have challenged physics-based modeling approaches fordecades.

Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration