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.
Generative inverse design of crystal struc- tures via diffusion models with transformers
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Reinforcement fine-tuning of a generative model produces new topological insulators and crystalline insulators, exemplified by Ge2Bi2O6 with a 0.26 eV full band gap.
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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Design Topological Materials by Reinforcement Fine-Tuned Generative Model
Reinforcement fine-tuning of a generative model produces new topological insulators and crystalline insulators, exemplified by Ge2Bi2O6 with a 0.26 eV full band gap.