Presented by: Juno Nam

We introduce LiFlow, a generative acceleration framework designed for efficiently simulating diffusive dynamics in solids, particularly lithium-based solid-state electrolytes (SSEs). LiFlow consists of two components: Propagator and Corrector, which utilize a conditional flow matching scheme to predict atomic displacements and perform denoising, respectively. Our model achieves a Spearman’s rank correlation of approximately 0.7 for the lithium mean squared displacement (MSD) on test set based on composition and temperature splits and offers a substantial speedup compared to reference molecular dynamics (MD) simulations using machine learning interatomic potentials (MLIPs). This framework facilitates high-throughput virtual screening for electrolyte materials and holds promise for the optimization of the kinetic properties of crystalline solids.