FlowMM: Generating Materials with Riemannian Flow Matching
This work addresses the computational challenges in materials science for discovering new materials, though it appears incremental as it builds on existing generative models with adaptations for crystal symmetries.
The authors tackled the problem of generating stable crystalline materials, which is computationally challenging due to the rarity of thermodynamically stable atomic arrangements, and achieved state-of-the-art performance in predicting stable structures and proposing novel compositions, with FlowMM being about 3x more efficient in finding stable materials compared to previous methods.
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.