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.Howev...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 materials modeling tasks for nanoporous materials,including structural optimization,molecular dynamics(MD)stability,bulk property prediction,and host-vip interactions.Evaluating 20 models from various architectures,we find that top-performing uMLIPs consistently outperform classical force fields and fine-tuned machine learning potentials across all tasks,demonstrating their readiness for deployment in nanoporous materials modeling.Our analysis highlights that data quality plays a more critical role than model architecture in determining performance across all evaluated uMLIPs.We release our modular and extensible benchmarking framework at https://github.com/AI4ChemS/mofsim-bench,providing an open resource to guide the adoption for nanoporous materials modeling and further development of uMLIPs.展开更多
基金support provided by the SciNet HPC Consortium and the Digital Research Alliance of Canadaas well as support from the state of Baden-Württemberg through bwHPC+2 种基金The project received financial support from the University of Toronto's Acceleration Consortium through the Canada First Research Excellence Fund under Grant number CFREF-2022-00042S.M.M. research program receives financial support from Natural Sciences and Engineering Research Council of Canada (NSERC) through the discovery programJ.H. acknowledges support from the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a program of Schmidt Sciences.
文摘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 materials modeling tasks for nanoporous materials,including structural optimization,molecular dynamics(MD)stability,bulk property prediction,and host-vip interactions.Evaluating 20 models from various architectures,we find that top-performing uMLIPs consistently outperform classical force fields and fine-tuned machine learning potentials across all tasks,demonstrating their readiness for deployment in nanoporous materials modeling.Our analysis highlights that data quality plays a more critical role than model architecture in determining performance across all evaluated uMLIPs.We release our modular and extensible benchmarking framework at https://github.com/AI4ChemS/mofsim-bench,providing an open resource to guide the adoption for nanoporous materials modeling and further development of uMLIPs.
