Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

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Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials (2018, background): “We benchmark the proposed edge update network on QM9 molecules, Materials Project (Jain et al., 2013) and OQMD (Saal et al., 2013; Kirklin et al., 2015) datasets and it shows an improvement over current state of the art results on all three datasets.”
An autonomous laboratory for the accelerated synthesis of inorganic materials (2023, positive): “Because the A-Lab handles samples in open air, we only considered targets that are predicted not to react with O 2 , CO 2 and H 2 O (Methods).”
A critical review of high entropy alloys and related concepts (2016, methodology): “Materials databases are being constructed to support this need [36,170].”
An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage (2020, background): “If considering bulk material properties (not surfaces or adsorbates), [135] is a benchmark across 13 diverse material properties with over 100,000 samples, OQMD [136] contains information on over 600,000 materials, and AFLOW [137] studies millions of materials.”