Unsupervised manifold learning on ICSD data reveals a low-dimensional embedding that segregates superconductors and predicts critical temperatures across families.
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A review of generative AI for inverse design of inorganic compounds, analyzing adaptations for their complexity in composition, geometry, symmetry, and electronic structure, with discussion of future benchmarks and synthesizability metrics.
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Charting the emergent low-dimensional manifold of quantum materials
Unsupervised manifold learning on ICSD data reveals a low-dimensional embedding that segregates superconductors and predicts critical temperatures across families.
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Inverse Design of Inorganic Compounds with Generative AI
A review of generative AI for inverse design of inorganic compounds, analyzing adaptations for their complexity in composition, geometry, symmetry, and electronic structure, with discussion of future benchmarks and synthesizability metrics.