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We propose an element-agnostic set of descriptors to model superalloyproperties with Gaussian process regression. Furthermore, we develop acorrection method to deliver the best and most physical predictions formicrochemistry in multi-phase alloys. The models' performance in predictions isconfirmed for superalloy microchemistry, microstructure, and strengthproperties. When including new, unseen elements in the test data, the modelsstill give good predictions; such extrapolations into new chemical-space wouldbe impossible with component-based descriptors.

Machine learning superalloy microchemistry and creep strength from physical descriptors