| Summary: | Materials chemistry is being profoundly influenced by the uptake of machine learning methodologies. Machine learning techniques, in combination with established techniques from computational physics, promise to accelerate the discovery of new materials by elucidating complex structure–property relationships from massive material databases. Despite exciting possibilities, further methodological developments call for a greater synergism between materials chemists, physicists, and engineers on one side, with computer science and math majors on the other. In this review, we provide a non-exhaustive account of machine learning in materials chemistry for computer scientists and applied mathematicians, with an emphasis on molecule datasets and materials chemistry problems. The first part of this review provides a tutorial on how to prepare such datasets for subsequent model building, with an emphasis on the construction of feature vectors. We also provide a self-contained introduction to density functional theory, a method from computational physics which is widely used to generate datasets and compute response variables. The second part reviews two machine learning methodologies which represent the status quo in materials chemistry at present – kernelized machine learning and Bayesian machine learning – and discusses their application to real datasets. In the third part of the review, we introduce some emerging machine learning techniques which have not been widely adopted by materials scientists and therefore present potential avenues for computer science and applied math majors. In the final concluding section, we discuss some recent machine learning-based approaches to real materials discovery problems and speculate on some promising future directions.
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