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Conventional machine learning approaches accelerate inorganic materials design via accurate property prediction and targeted material generation, yet they operate as single-shot models limited by the latent knowledge baked into their training data.
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Materials design and discovery with high-throughput density functional theory: the open quantum materials database (oqmd)
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