A joint diffusion framework for crystal structures and local electronic descriptors improves inverse materials design success rates and structural quality over structure-only models under band-gap and formation-energy conditioning.
Integrating electronic structure into generative modeling of inorganic materials
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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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.
citing papers explorer
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Inverse Materials Design via Joint Generation of Crystal Structures and Local Electronic Descriptors
A joint diffusion framework for crystal structures and local electronic descriptors improves inverse materials design success rates and structural quality over structure-only models under band-gap and formation-energy conditioning.
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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.