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Reinforcement learning steers generative crystal design
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DOI：10.1038/s42256-026-01282-0.png)
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Abstract
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Generative machine learning methods have led to progress in crystal discovery, but cannot fully explore the space of material candidates that are both novel and useful. A reinforcement learning-based method steers candidate generation to these areas, enabling the design of novel functional materials.
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