A reinforcement learning policy guides kinetic Monte Carlo transition selection with an importance-sampling correction, claiming single-GPU billion-atom Fe-Cu simulations that previously required a supercomputer.
Atomistic modeling of radiation damage in crystalline materials
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SwarmThinkers: Learning Physically Consistent Atomic KMC Transitions at Scale
A reinforcement learning policy guides kinetic Monte Carlo transition selection with an importance-sampling correction, claiming single-GPU billion-atom Fe-Cu simulations that previously required a supercomputer.