The Monte Carlo(MC)method is widely used to simulate kinetic processes involving particle hopping through probabilistic modeling and stochastic sampling,particularly in contexts relevant to electrochemical energy stor...The Monte Carlo(MC)method is widely used to simulate kinetic processes involving particle hopping through probabilistic modeling and stochastic sampling,particularly in contexts relevant to electrochemical energy storage,spanning material synthesis,microstructural evolution,and device-level operation.However,the broader applicability of MC simulations is often limited by the requirement for customized definitions of key parameters for each specific physical system.To address this limitation,we propose an adaptive Monte Carlo simulation framework(AMCSF),which adjusts hopping rates,interaction energies,and configuration state parameters on-the-fly in response to updating system states.We provide three representative examples of the kinetic process simulation to demonstrate its potential utility and broad applications,including effective carrier ion concentration analysis in garnet-type electrolytes,voltage plateau formation in phosphate-based mixed ionic conductor electrodes,and oxygen release in lithium-rich layered oxide cathodes.The work provides a paradigm towards synergizing modeling and experiments into the understanding of complex materials kinetics and lays the groundwork for hierarchically bridging multiscale modeling methods.展开更多
基金supported by the National Natural Science Foundation of China(Nos.92472207,52372208,52472223)the Science and Technology Commission of Shanghai Municipality(Grant No.22160730100)+1 种基金the High Performance Computing Center of Shanghai University,Shanghai Engineering Research Center of Intelligent Computing System(Grant No.19DZ2252600)the Shanghai Technical Service Center for Advanced Ceramics Structure Design and Precision Manufacturing(Grant No.20DZ2294000)。
文摘The Monte Carlo(MC)method is widely used to simulate kinetic processes involving particle hopping through probabilistic modeling and stochastic sampling,particularly in contexts relevant to electrochemical energy storage,spanning material synthesis,microstructural evolution,and device-level operation.However,the broader applicability of MC simulations is often limited by the requirement for customized definitions of key parameters for each specific physical system.To address this limitation,we propose an adaptive Monte Carlo simulation framework(AMCSF),which adjusts hopping rates,interaction energies,and configuration state parameters on-the-fly in response to updating system states.We provide three representative examples of the kinetic process simulation to demonstrate its potential utility and broad applications,including effective carrier ion concentration analysis in garnet-type electrolytes,voltage plateau formation in phosphate-based mixed ionic conductor electrodes,and oxygen release in lithium-rich layered oxide cathodes.The work provides a paradigm towards synergizing modeling and experiments into the understanding of complex materials kinetics and lays the groundwork for hierarchically bridging multiscale modeling methods.
