Ab initio molecular dynamics(AIMD)simulation is widely employed in studying diffusion mechanisms and in quantifying diffusional properties of materials.However,AIMD simulations are often limited to a few hundred atoms...Ab initio molecular dynamics(AIMD)simulation is widely employed in studying diffusion mechanisms and in quantifying diffusional properties of materials.However,AIMD simulations are often limited to a few hundred atoms and a short,sub-nanosecond physical timescale,which leads to models that include only a limited number of diffusion events.As a result,the diffusional properties obtained from AIMD simulations are often plagued by poor statistics.In this paper,we re-examine the process to estimate diffusivity and ionic conductivity from the AIMD simulations and establish the procedure to minimize the fitting errors.In addition,we propose methods for quantifying the statistical variance of the diffusivity and ionic conductivity from the number of diffusion events observed during the AIMD simulation.Since an adequate number of diffusion events must be sampled,AIMD simulations should be sufficiently long and can only be performed on materials with reasonably fast diffusion.We chart the ranges of materials and physical conditions that can be accessible by AIMD simulations in studying diffusional properties.Our work provides the foundation for quantifying the statistical confidence levels of diffusion results from AIMD simulations and for correctly employing this powerful technique.展开更多
Machine learning interatomic potentials(MLIPs)are a promising technique for atomic modeling.While small errors are widely reported for MLIPs,an open concern is whether MLIPs can accurately reproduce atomistic dynamics...Machine learning interatomic potentials(MLIPs)are a promising technique for atomic modeling.While small errors are widely reported for MLIPs,an open concern is whether MLIPs can accurately reproduce atomistic dynamics and related physical properties in molecular dynamics(MD)simulations.In this study,we examine the state-of-the-art MLIPs and uncover several discrepancies related to atom dynamics,defects,and rare events(REs),compared to ab initio methods.We find that low averaged errors by current MLIP testing are insufficient,and develop quantitative metrics that better indicate the accurate prediction of atomic dynamics by MLIPs.The MLIPs optimized by the RE-based evaluation metrics are demonstrated to have improved prediction in multiple properties.The identified errors,the evaluation metrics,and the proposed process of developing such metrics are general to MLIPs,thus providing valuable guidance for future testing and improvements of accurate and reliable MLIPs for atomistic modeling.展开更多
基金The authors acknowledge the support from Office of Naval Research(ONR)and from National Science Foundation under award No.1550423This research used computational facilities from the University of Maryland supercomputing resources,the Maryland Advanced Research Computing Center(MARCC),and the Extreme Science and Engineering Discovery Environment(XSEDE)supported by National Science Foundation Award No.DMR150038.
文摘Ab initio molecular dynamics(AIMD)simulation is widely employed in studying diffusion mechanisms and in quantifying diffusional properties of materials.However,AIMD simulations are often limited to a few hundred atoms and a short,sub-nanosecond physical timescale,which leads to models that include only a limited number of diffusion events.As a result,the diffusional properties obtained from AIMD simulations are often plagued by poor statistics.In this paper,we re-examine the process to estimate diffusivity and ionic conductivity from the AIMD simulations and establish the procedure to minimize the fitting errors.In addition,we propose methods for quantifying the statistical variance of the diffusivity and ionic conductivity from the number of diffusion events observed during the AIMD simulation.Since an adequate number of diffusion events must be sampled,AIMD simulations should be sufficiently long and can only be performed on materials with reasonably fast diffusion.We chart the ranges of materials and physical conditions that can be accessible by AIMD simulations in studying diffusional properties.Our work provides the foundation for quantifying the statistical confidence levels of diffusion results from AIMD simulations and for correctly employing this powerful technique.
基金The authors acknowledge the funding support from National Science Foundation Award#1940166 and 2004837 the computational facilities from the University of Maryland supercomputing resources,and the Maryland Advanced Research Computing Center(MARCC).
文摘Machine learning interatomic potentials(MLIPs)are a promising technique for atomic modeling.While small errors are widely reported for MLIPs,an open concern is whether MLIPs can accurately reproduce atomistic dynamics and related physical properties in molecular dynamics(MD)simulations.In this study,we examine the state-of-the-art MLIPs and uncover several discrepancies related to atom dynamics,defects,and rare events(REs),compared to ab initio methods.We find that low averaged errors by current MLIP testing are insufficient,and develop quantitative metrics that better indicate the accurate prediction of atomic dynamics by MLIPs.The MLIPs optimized by the RE-based evaluation metrics are demonstrated to have improved prediction in multiple properties.The identified errors,the evaluation metrics,and the proposed process of developing such metrics are general to MLIPs,thus providing valuable guidance for future testing and improvements of accurate and reliable MLIPs for atomistic modeling.
