Fig. 2: Flowcharts of materials design in MLMD platform.

a Model inference involves establishing an ML model, generating visual samples, executing model inference, and subsequently verifying the recommended new materials through experiment. b Surrogate optimization entails establishing ML models, incorporating feature physical constraints, selecting heuristic optimization algorithms, and subsequently verifying the recommended new materials through experiment. c Active learning involves utilizing GPR model, creating a virtual sample space, selecting a suitable utility function, and ultimately verifying the recommended new materials through experiment. Notably, the results from experiments on the new materials of the three methods can be iterated into the next loop for building more better ML model.