Research paperComputational MLIPComputational MDComputational AimdDeep potential for interaction between hydrated Cs+ and grapheneYangjun Qin, Liuhua Mu, Xiao Wan, Zhicheng Zong et al.2024·10.1021/acs.langmuir.5c00508·arXiv:2408.15797AbstractA deep neural network potential is developed for hydrated Cs+ interacting with graphene. The potential is trained and validated against DFT data from AIMD-generated configurations, then used to study vibrational power spectra, radial distribution functions, mean square displacement, adsorption energy, charge transfer, and the effect of bound water on hydrated Cs+-graphene interactions.Read more
Hydrated Cs+ interacting with graphene, represented in the deep potential training/validation workflow and subsequent MD simulations.2 propertiesSimulated Configuration MlipCStudied MaterialCs+Studied MaterialH₂OStudied MaterialExpand
Research paperComputational MLIPComputational MDComputational AimdDeep potential for interaction between hydrated Cs+ and grapheneYangjun Qin, Liuhua Mu, Xiao Wan, Zhicheng Zong et al.2024·10.1021/acs.langmuir.5c00508·arXiv:2408.15797AbstractA deep neural network potential is developed for hydrated Cs+ interacting with graphene. The potential is trained and validated against DFT data from AIMD-generated configurations, then used to study vibrational power spectra, radial distribution functions, mean square displacement, adsorption energy, charge transfer, and the effect of bound water on hydrated Cs+-graphene interactions.Read more
Hydrated Cs+ interacting with graphene, represented in the deep potential training/validation workflow and subsequent MD simulations.2 propertiesSimulated Configuration MlipCStudied MaterialCs+Studied MaterialH₂OStudied MaterialExpand
Research paperComputational MLIPComputational MDComputational AimdDeep potential for interaction between hydrated Cs+ and grapheneYangjun Qin, Liuhua Mu, Xiao Wan, Zhicheng Zong et al.2024·10.1021/acs.langmuir.5c00508·arXiv:2408.15797AbstractA deep neural network potential is developed for hydrated Cs+ interacting with graphene. The potential is trained and validated against DFT data from AIMD-generated configurations, then used to study vibrational power spectra, radial distribution functions, mean square displacement, adsorption energy, charge transfer, and the effect of bound water on hydrated Cs+-graphene interactions.Read more
Hydrated Cs+ interacting with graphene, represented in the deep potential training/validation workflow and subsequent MD simulations.2 propertiesSimulated Configuration MlipCStudied MaterialCs+Studied MaterialH₂OStudied MaterialExpand
Research paperComputational MLIPComputational MDComputational AimdDeep potential for interaction between hydrated Cs+ and grapheneYangjun Qin, Liuhua Mu, Xiao Wan, Zhicheng Zong et al.2024·10.1021/acs.langmuir.5c00508·arXiv:2408.15797AbstractA deep neural network potential is developed for hydrated Cs+ interacting with graphene. The potential is trained and validated against DFT data from AIMD-generated configurations, then used to study vibrational power spectra, radial distribution functions, mean square displacement, adsorption energy, charge transfer, and the effect of bound water on hydrated Cs+-graphene interactions.Read more
Hydrated Cs+ interacting with graphene, represented in the deep potential training/validation workflow and subsequent MD simulations.2 propertiesSimulated Configuration MlipCStudied MaterialCs+Studied MaterialH₂OStudied MaterialExpand