Quantum-accurate machine learning potentials for metal-organic frameworks using temperature driven active learning  

作　　者：Abhishek Sharma Stefano Sanvito 

机构地区：[1]School of Physics,AMBER and CRANN Institute,Trinity College,Dublin 2,Ireland

出　　处：《npj Computational Materials》2024年第1期737-749,共13页计算材料学(英文)

基　　金：supported by Science Foundation Ireland through the Advanced Materials and BioEngineering Research(AMBER)(Grant:12/RC/2278−P2);by the Qatar National Research Fund(Award:NPRP12C-0821-190017).

摘　　要：Understanding structural flexibility of metal-organic frameworks(MOFs)via molecular dynamics simulations is crucial to design better MOFs.Density functional theory(DFT)and quantum-chemistry methods provide highly accuratemolecular dynamics,but the computational overheads limit their use in long time-dependent simulations.In contrast,classical force fields struggle with the description of coordination bonds.Here we develop a DFT-accurate machine-learning spectral neighbor analysis potentials for two representative MOFs.Their structural and vibrational properties are then studied and tightly compared with available experimental data.Most importantly,we demonstrate an activelearning algorithm,based on mapping the relevant internal coordinates,which drastically reduces the number of training data to becomputed at theDFT level.Thus,the workflowpresented here appears as an efficient strategy for the study of flexible MOFs with DFT accuracy,but at a fraction of the DFT computational cost.

关 键 词：BONDS VIBRATIONAL COORDINATES 

分 类 号：O64[理学—物理化学]