Flow Matching for Accelerated Simulation of Atomic Transport in Materials
<|reference_start|>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 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.<|reference_end|>
@article{nam2024flow,
    title={Flow Matching for Accelerated Simulation of Atomic Transport in
  Materials},
    author={Juno Nam, Sulin Liu, Gavin Winter, KyuJung Jun, Soojung Yang, Rafael
  G'omez-Bombarelli},
    journal={arXiv preprint arXiv:2410.01464},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01464},
    primaryClass={cond-mat.mtrl-sci cs.LG physics.comp-ph}
}