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
In the realm of lithium superionic conductors,pursuing higher ionic conductivity is imperative,with the variance in lithium-ion concentration playing a determining role.Due to the permanent and temporary site-blocking...In the realm of lithium superionic conductors,pursuing higher ionic conductivity is imperative,with the variance in lithium-ion concentration playing a determining role.Due to the permanent and temporary site-blocking effects,especially at non-dilute concentrations,not all Li-ions contribute to ionic conductivity.Here,we propose a strategy to directly calculate effective mobile ion concentration in which multiple-ion correlated migration is considered in the percolation analysis with the input of Li-ion distributions and hopping behavior based on kinetic Monte Carlo simulation,termed P-KMC.We provide examples of two representative lithium superionic conductors,cubic garnet-type LixA3B2O12(0≤x≤9;A and B represent different cations)and perovskite-type LixLa2/3−x/3TiO3(0≤x≤0.5),to demonstrate the direct dependence of the ionic conductivity on the effective mobile ion concentration.This methodology provides a robust tool to identify the optimal compositions for the highest ionic conductivity in superionic conductors.展开更多
The electrochemical thermodynamic and kinetic characteristics of rechargeable batteries are critically influenced by the ordering of mobile ions in electrodes or solid electrolytes.However,because of the experimental ...The electrochemical thermodynamic and kinetic characteristics of rechargeable batteries are critically influenced by the ordering of mobile ions in electrodes or solid electrolytes.However,because of the experimental difficulty of capturing the lighter migration ion coupled with the theoretical limitation of searching for ordered phases in a constrained cell,predicting stable ordered phases involving cell transformations or at extremely dilute concentrations remains challenging.Here,a group-subgroup transformation method based on lattice transformation and Wyckoff-position splitting is employed to predict the ordered ground states.We reproduce the previously reported Li_(0.75)CoO_(2),Li_(0.8333)CoO_(2),and Li_(0.8571)CoO_(2)phases and report a new Li_(0.875)CoO_(2)ground state.Taking the advantage of Wyckoff-position splitting in reducing the number of configurations,we identify the stablest Li_(0.0625)C_(6) dilute phase in Li-ion intercalated graphite.We also resolve the Li/La/vacancy ordering in Li_(3x)La_(2/3−x)TiO_(3)(0<x<0.167),which explains the observed Li-ion diffusion anisotropy.These findings provide important insight towards understanding the rechargeable battery chemistry.展开更多
The original version of this Article contained error in DATA AVAILABILITY,in which the website hyperlink is not valid and should be revised to https://github.com/shuhebing/gsop.The same error also occurs in CODE AVAIL...The original version of this Article contained error in DATA AVAILABILITY,in which the website hyperlink is not valid and should be revised to https://github.com/shuhebing/gsop.The same error also occurs in CODE AVAILABILITY,in which the website hyperlink should also be revised to https://github.com/shuhebing/gsop.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.
基金supported by the National Natural Science Foundation of China(Nos.92270124,52102313,92472207)the Hunan Provincial Natural Science Foundation of China(No.2023JJ40635)+1 种基金Shandong Province Natural Science Foundation(No.ZR2022ZD11)the High-Performance Computing Center of Shanghai University and Shanghai Engineering Research Center of Intelligent Computing Systems for providing computing resources and technical support.
文摘In the realm of lithium superionic conductors,pursuing higher ionic conductivity is imperative,with the variance in lithium-ion concentration playing a determining role.Due to the permanent and temporary site-blocking effects,especially at non-dilute concentrations,not all Li-ions contribute to ionic conductivity.Here,we propose a strategy to directly calculate effective mobile ion concentration in which multiple-ion correlated migration is considered in the percolation analysis with the input of Li-ion distributions and hopping behavior based on kinetic Monte Carlo simulation,termed P-KMC.We provide examples of two representative lithium superionic conductors,cubic garnet-type LixA3B2O12(0≤x≤9;A and B represent different cations)and perovskite-type LixLa2/3−x/3TiO3(0≤x≤0.5),to demonstrate the direct dependence of the ionic conductivity on the effective mobile ion concentration.This methodology provides a robust tool to identify the optimal compositions for the highest ionic conductivity in superionic conductors.
基金This work is supported by the National Natural Science Foundation of China(Nos.11874254,51622207)the National Key Research and Development Program of China(No.2017YFB0701600).All the computations are performed on the highperformance computing platform provided by the High-Performance Computing Center of Shanghai University.
文摘The electrochemical thermodynamic and kinetic characteristics of rechargeable batteries are critically influenced by the ordering of mobile ions in electrodes or solid electrolytes.However,because of the experimental difficulty of capturing the lighter migration ion coupled with the theoretical limitation of searching for ordered phases in a constrained cell,predicting stable ordered phases involving cell transformations or at extremely dilute concentrations remains challenging.Here,a group-subgroup transformation method based on lattice transformation and Wyckoff-position splitting is employed to predict the ordered ground states.We reproduce the previously reported Li_(0.75)CoO_(2),Li_(0.8333)CoO_(2),and Li_(0.8571)CoO_(2)phases and report a new Li_(0.875)CoO_(2)ground state.Taking the advantage of Wyckoff-position splitting in reducing the number of configurations,we identify the stablest Li_(0.0625)C_(6) dilute phase in Li-ion intercalated graphite.We also resolve the Li/La/vacancy ordering in Li_(3x)La_(2/3−x)TiO_(3)(0<x<0.167),which explains the observed Li-ion diffusion anisotropy.These findings provide important insight towards understanding the rechargeable battery chemistry.
文摘The original version of this Article contained error in DATA AVAILABILITY,in which the website hyperlink is not valid and should be revised to https://github.com/shuhebing/gsop.The same error also occurs in CODE AVAILABILITY,in which the website hyperlink should also be revised to https://github.com/shuhebing/gsop.
