Computational modeling of physical processes in metal-organic frameworks(MOFs)is highly challenging due to the presence of spatial heterogeneities and complex operating conditions which affect their behavior.Density f...Computational modeling of physical processes in metal-organic frameworks(MOFs)is highly challenging due to the presence of spatial heterogeneities and complex operating conditions which affect their behavior.Density functional theory(DFT)may describe interatomic interactions at the quantum mechanical level,but is computationally too expensive for systems beyond the nanometer and picosecond range.Herein,we propose an incremental learning scheme to construct accurate and data-efficient machine learning potentials for MOFs.The scheme builds on the power of equivariant neural network potentials in combination with parallelized enhanced sampling and on-the-fly training to simultaneously explore and learn the phase space in an iterative manner.With only a few hundred single-point DFT evaluations per material,accurate and transferable potentials are obtained,even for flexible frameworks with multiple structurally different phases.The incremental learning scheme is universally applicable and may pave the way to model framework materials in larger spatiotemporal windows with higher accuracy.展开更多
基金S.V.and M.C.C.wish to thank the Research Foundation-Flanders(FWO)for doctoral fellowships(grant nos.11H6821N and 11D0420N respectively)The resources and services used in this work were provided by VSC(Flemish Supercomputer Center),funded by the Research Foundation-Flanders(FWO)and the Flemish Government。
文摘Computational modeling of physical processes in metal-organic frameworks(MOFs)is highly challenging due to the presence of spatial heterogeneities and complex operating conditions which affect their behavior.Density functional theory(DFT)may describe interatomic interactions at the quantum mechanical level,but is computationally too expensive for systems beyond the nanometer and picosecond range.Herein,we propose an incremental learning scheme to construct accurate and data-efficient machine learning potentials for MOFs.The scheme builds on the power of equivariant neural network potentials in combination with parallelized enhanced sampling and on-the-fly training to simultaneously explore and learn the phase space in an iterative manner.With only a few hundred single-point DFT evaluations per material,accurate and transferable potentials are obtained,even for flexible frameworks with multiple structurally different phases.The incremental learning scheme is universally applicable and may pave the way to model framework materials in larger spatiotemporal windows with higher accuracy.
