Efficient and Transferable Machine Learning Potentials for Zn-Coordinated Zeolitic Imidazolate Frameworks Through Structural Diversity Sampling and Transfer Learning

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.