MOFSimBench: evaluating universal machine learning interatomic potentials in metal-organic framework molecular modeling.

ABSTRACT: Universal machine learning interatomic potentials (uMLIPs) have emerged as powerful tools for accelerating atomistic simulations, offering scalable and efficient modeling with accuracy close to quantum calculations. However, their reliability and effectiveness in practical, real-world applications remain an open question. Metal-organic frameworks (MOFs) and related nanoporous materials are highly porous crystals with critical relevance in carbon capture, energy storage, and catalysis applications. Modeling nanoporous materials presents distinct challenges for uMLIPs due to their diverse chemistry, structural complexity, including porosity and coordination bonds, and the absence from existing training datasets. Here, we introduce MOFSimBench, a benchmark for evaluating uMLIPs on key materia

SUBMITTER: Kraß H 

PROVIDER: S-EPMC12774856 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature