Data-Driven Materials Discovery and Synthesis using Machine Learning Methods

Abstract

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.