Atomistic calculations and materials informatics: A review

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Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns (2019, unknown): “ML provides the tools to scrutinize and extract useful information to be employed in modeling and system-solving solutions (Artrith and Urban, 2016; Ward and Wolverton, 2017).”
Opportunities and Challenges for Machine Learning in Materials Science (2020, methodology): “(153) Sheridan, R. P. Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.”
A perspective on conventional high-temperature superconductors at high pressure: Methods and materials (2019, background): “Scientists seem to embrace the inclusion of these methods in their research and the number of reported applications in material science is growing at an extraordinary rate (see References [364, 365, 366]).”