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FlowMM: Generating Materials with Riemannian Flow Matching
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Crystalline materials are a fundamental component in next-generation technologies, yet modeling their distribution presents unique computational challenges. Of the plausible arrangements of atoms in a periodic lattice only a vanishingly small percentage are thermodynamically stable, which is a key indicator of the materials that can be experimentally realized. Two fundamental tasks in this area are to (a) predict the stable crystal structure of a known composition of elements and (b) propose novel compositions along with their stable structures. We present FlowMM, a pair of generative models that achieve state-of-the-art performance on both tasks while being more efficient and more flexible than competing methods. We generalize Riemannian Flow Matching to suit the symmetries inherent to crystals: translation, rotation, permutation, and periodic boundary conditions. Our framework enables the freedom to choose the flow base distributions, drastically simplifying the problem of learning crystal structures compared with diffusion models. In addition to standard benchmarks, we validate FlowMM's generated structures with quantum chemistry calculations, demonstrating that it is about 3x more efficient, in terms of integration steps, at finding stable materials compared to previous open methods.
Forward citations
Cited by 7 Pith papers
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Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion
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MCFlow uses decoupled flow time axes for atom types and crystal structures so a single model handles crystal structure prediction, de novo generation, and atom-type generation.
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Conditioning the attention layers of a crystal-writing transformer on continuous property values enables XRD-based structure recovery and targeted generation of photovoltaic candidates.
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Open Materials Generation with Stochastic Interpolants
By tuning stochastic interpolants for periodic crystals and coupling them with discrete flow matching, OMatG sets new state-of-the-art results on crystal structure prediction and de novo materials generation.
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CrystalGRW: Generative Modeling of Crystal Structures with Targeted Properties via Geodesic Random Walks
A Riemannian diffusion model with an equivariant graph network generates crystal structures that sit close to DFT ground states and can be steered toward specified point groups.
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