newsNature Machine IntelligenceTrust 88 · LabPublished 11d agoLive · 9d ago
Guiding generative models to uncover diverse and novel crystals via reinforcement learning
Nature Machine Intelligence, Published online: 06 July 2026; doi:10.1038/s42256-026-01262-4 Park and Walsh introduce a reinforcement learning framework that could accelerate the discovery of new, thermodynamically stable and diverse crystalline materials with desired properties.
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- PossiblePossibly related (embedding) · 46%One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective →
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paperBeyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmarkrepojanosh/matbench-discoverypaperGraph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual RecombinationpaperOne More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspectiverepopytorch/rl
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repopytorch/rlpaperBeyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) BenchmarkpaperOne More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspectiverepojanosh/matbench-discoveryrepoelectrocatalysis-group/atomic-recipespaperActive rejection enables reliable generalization of universal machine-learning interatomic potentialspaperCatRetriever: Contrastive Representation Learning for Slab-to-Bulk Retrieval in Generative Catalyst DiscoverypaperGraph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination