AI Article Synopsis
- Diffusion in alloys is a crucial atomic process, but traditional atomistic simulations face limitations because they can’t capture long timescales needed for experiments.
- This study introduces reinforcement learning (RL) methods to enhance simulation capabilities, showcasing two specific techniques: RL transition kinetics simulator (TKS) and RL low-energy states sampler (LSS).
- The authors tested these methods by calculating hydrogen diffusivity in both pure metals and a CrCoNi alloy, revealing unexpected behaviors and demonstrating that RL LSS is more efficient than conventional algorithms for sampling low-energy states.
Article Abstract
Diffusion in alloys is an important class of atomic processes. However, atomistic simulations of diffusion in chemically complex solids are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interest. In this work, long-timescale simulation methods are developed using reinforcement learning (RL) that extends simulation capability to match the duration of experimental interest. Two special limits, RL transition kinetics simulator (TKS) and RL low-energy states sampler (LSS), are implemented and explained in detail, while the meaning of general RL are also discussed. As a testbed, hydrogen diffusivity is computed using RL TKS in pure metals and a medium entropy alloy, CrCoNi, and compared with experiments. The algorithm can produce counter-intuitive hydrogen-vacancy cooperative motion. We also demonstrate that RL LSS can accelerate the sampling of low-energy configurations compared to the Metropolis-Hastings algorithm, using hydrogen migration to copper (111) surface as an example.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10837361 | PMC |
| http://dx.doi.org/10.1002/advs.202304122 | DOI Listing |
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