Transforming MOF modeling with machine-learned potentials: progress and perspectives

Abstract

Machine-learned potentials (MLPs) have emerged as transformative tools for modeling metal-organic frameworks (MOFs), bridging the accuracy of quantum mechanics with the efficiency required for large-scale molecular simulations. By learning the potential energy surface directly from quantum-mechanical reference data, MLPs enable a unified description of the complex nature of MOFs and their interactions with guest molecules across multiple length and time scales. Recent developments have demonstrated the capability of MLPs to model intrinsic MOF properties such as lattice dynamics, thermal expansion, and mechanical response, as well as to describe adsorption thermodynamics, diffusion, and cooperative host-guest behavior in flexible frameworks. Developing reliable and transferable MLPs for MOFs remains a significant challenge due to the vast chemical and structural diversity of MOFs and the complexity of sampling guest-framework configurations. The lack of openly shared, standardized, and user-friendly MLP implementations also limits their broader adoption. This review focuses on the current progress in MLP-based modeling of MOFs, highlighting methodological advances, data-generation strategies, and active-learning protocols, while outlining the key challenges and future directions for developing transferable, accessible, and universal MLPs for the predictive design and discovery of MOFs.