  Experimentally [1-38] and computationally [39-50] validated machine learning
(ML) articles are sorted based on the size of the training data: 1-100,
101-10000, and 10000+ in a comprehensive set summarizing legacy and recent
advances in the field. The review emphasizes the interrelated fields of
synthesis, characterization, and prediction. Size range 1-100 consists mostly
of Bayesian optimization (BO) articles, whereas 101-10000 consists mostly of
support vector machine (SVM) articles. The articles often use combinations of
ML, feature selection (FS), adaptive design (AD), high-throughput (HiTp)
techniques, and domain knowledge to enhance predictive performance and/or model
interpretability. Grouping cross-validation (G-CV) techniques curb overly
optimistic extrapolative predictive performance. Smaller datasets relying on AD
are typically able to identify new materials with desired properties but do so
in a constrained design space. In larger datasets, the low-hanging fruit of
materials optimization is typically already discovered, and the models are
generally less successful at extrapolating to new materials, especially when
the model training data favors a particular type of material. The large
increase of ML materials science articles that perform experimental or
computational validation on the predicted results demonstrates the
interpenetration of materials informatics with the materials science discipline
and an accelerating materials discovery for real-world applications.
