A roadmap for machine learning in alloy modeling

Years before the data science craze, ideas of modern machine learning played an essential role in alloy modeling. Genetic algorithms (for both searching materials space and model construction), statistical learning methods based on Bayesian ideas, dimensionality reduction approaches (cluster expansion, compressive sensing, interatomic potentials, etc.) have contributed to a rich heritage of innovation in the field. Recent developments in data science, and the affordability of generating unprecedented volumes of high-quality training data, open up further avenues. New materials informatics approaches, machine-learned interatomic potentials (GAP, SNAP, MTP, genetic programs, atomic cluster expansion, and others), new thermodynamic approaches such as nested sampling, etc., provide unrivaled opportunities in computational alloy modeling and discovery. We highlight past successes and spotlight promising new approaches.