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 Structures via Diffusion Models with Transformers
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
A GPT-style model pretrained on 133M catalyst structures generates valid structures conditioned on categorical and continuous properties, achieving 98% structural validity and up to 4-fold screening efficiency gains.
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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Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining
A GPT-style model pretrained on 133M catalyst structures generates valid structures conditioned on categorical and continuous properties, achieving 98% structural validity and up to 4-fold screening efficiency gains.
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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.