Efficient and Transferable Machine Learning Potentials for Zn-Coordinated Zeolitic Imidazolate Frameworks Through...

Zeolitic Imidazolate Frameworks (ZIFs) exhibit remarkable structural flexibility that governs many of their functional properties, yet accurately describing their dynamic behavior remains a significant computational challenge. Classical force fields often fail to capture framework flexibility, whereas first-principles methods are prohibitively expensive. In this work, high-fidelity machine learning potentials (MLPs) are first developed for ZIF-8 using the equivariant Allegro architecture. To reduce the cost associated with the development of MLPs, an active-learning-inspired structural selection strategy based on the smooth overlap of atomic positions (SOAP) is implemented, allowing DFT-level accuracy to be achieved with fewer training configurations. Benchmarking against currently available models demonstrates that, while those models also offer reasonable energy predictions, their force errors remain 1 order of magnitude larger than the specialized MLP developed herein with a force mean absolute error of as small as 0.016 eV/Å. Building upon these findings, a transferable MLP spanning diverse Zn-based ZIFs is further constructed. Moreover, a hierarchical transfer learning workflow is also established to enable rapid adaptation to unseen topologies with improved accuracy using only a limited number of additional configurations. As a demonstration, MD simulations powered by the developed MLP successfully capture methane-induced gate-opening in ZIF-8, resolving the structural transition at subnanosecond resolution. Overall, this work provides a transferable, DFT-accurate machine learning force field for Zn-based ZIFs.