Flow Matching for Accelerated Simulation of Atomic Transport in Materials
We introduce LiFlow, a generative framework to accelerate molecular dynamics (MD) simulations for crystalline materials that formulates the task as conditional generation of atomic displacements. The model uses flow matching, with a Propagator submodel to generate atomic displacements and a Correcto...
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Zusammenfassung: | We introduce LiFlow, a generative framework to accelerate molecular dynamics
(MD) simulations for crystalline materials that formulates the task as
conditional generation of atomic displacements. The model uses flow matching,
with a Propagator submodel to generate atomic displacements and a Corrector to
locally correct unphysical geometries, and incorporates an adaptive prior based
on the Maxwell-Boltzmann distribution to account for chemical and thermal
conditions. We benchmark LiFlow on a dataset comprising 25-ps trajectories of
lithium diffusion across 4,186 solid-state electrolyte (SSE) candidates at four
temperatures. The model obtains a consistent Spearman rank correlation of
0.7-0.8 for lithium mean squared displacement (MSD) predictions on unseen
compositions. Furthermore, LiFlow generalizes from short training trajectories
to larger supercells and longer simulations while maintaining high accuracy.
With speed-ups of up to 600,000$\times$ compared to first-principles methods,
LiFlow enables scalable simulations at significantly larger length and time
scales. |
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DOI: | 10.48550/arxiv.2410.01464 |