Do Learned Particle Dynamics Transfer Across Battery Operating Conditions?

Machine-learned simulators of physical systems are usually trained and tested under similar conditions, yet for batteries what matters is how electrode particles behave when the charging rate, temperature or cell age changes, and such measurements are scarce. We introduce a benchmark of operando optical microscopy movies, recorded while the batteries run, in which the same particles are filmed under different conditions: 22 graphite particles at slow (C/20) and faster (C/2) charging over 6–45 °C (about 300 hours of recording), 24 particles of a nickel manganese cobalt oxide (NMC) cathode at three rates, and four further cells. Forecasters predict a particle's future image from its recent frames and the known current, and are compared with baselines that replay the same particle's earlier behaviour. First, with one training rate, a network that steps forward in charge passed rather than in time transfers far better from slow to fast charging (graphite: skill gains of 0.42 and 0.51 over matched time steps at short and long horizons), because charge builds in the known speed-up. Second, same-particle baselines largely catch up with a step-by-step network at long horizons, whereas a U-Net that predicts each horizon directly stays ahead on six graphite rate and temperature splits but does not clearly beat NMC's best baselines. Third, particles first convert during lithiation in a reproducible order at one rate and temperature but not detectably across rates, and forecasts from slow to fast charging miss when and where particles convert, relative to a repeat experiment. For the foundation model Poseidon, performance depends strongly on how the output is scaled; Poseidon beats the direct U-Net at short horizons when data are scarce, but under the tested recipes neither Poseidon nor Walrus beats it at long horizons.