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

ABSTRACT: 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 accurate molecular 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 active-learning algorithm, based on mapping the relevant internal coordinates, which drastically reduces the numb

SUBMITTER: Sharma A 

PROVIDER: S-EPMC11461275 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature