Diffusion-based deep generative models have emerged as powerful tools for inverse materials design. Yet many existing approaches overlook essential chemical constraints, such as oxidation-state balance, which can lead to chemically invalid structures. Here we introduce 'crystal generator with valence-constrained design...
JANUS is a multimodal neural sampler that couples continuous and masked discrete diffusion through an equivariant graph neural network trained directly from energy evaluations, without pre-generated equilibrium data, providing a foundation for thermodynamic sampling, characterization and inverse design of chemically di...
Denis Blessing, Mou-Yang Cheng, M. Schebek et al.· 2 citations
LATAS, an efficient sampler that learns a diffusion process to generate Boltzmann-distributed amorphous structures directly from a target energy function, is introduced, establishing ATLAS as a foundation model for sampling, steering and designing amorphous materials.
Mouyang Cheng, Denis Blessing, Botao Yu et al.· 1 citation
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