  We propose an element-agnostic set of descriptors to model superalloy
properties with Gaussian process regression. Furthermore, we develop a
correction method to deliver the best and most physical predictions for
microchemistry in multi-phase alloys. The models' performance in predictions is
confirmed for superalloy microchemistry, microstructure, and strength
properties. When including new, unseen elements in the test data, the models
still give good predictions; such extrapolations into new chemical-space would
be impossible with component-based descriptors.
