High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds

By A. Oliynyk, Erin Antono, Taylor D. Sparks, Leila Ghadbeigi, M. Gaultois, Bryce Meredig, A. Mar. 2016

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Machine learning for molecular and materials science (2018, methodology): “One method predicts the likelihood a given composition will adopt the Heusler structure and is trained on experimental data.(73) This approach was applied to screen hypothetical compositions and successfully identified 12 new gallide”
Machine Learning and Materials Informatics: Recent Applications and Prospects (2017, methodology): “Likewise, Oliynyk and co-workers recently used a set of elemental descriptors to train a machine-learning model, built on a random forest algorithm [25], with an aim to accelerate the search for Heusler compounds.”