Assessing UFF and DFT-Tuned Force Fields for Predicting Experimental Isotherms of MOFs

Metal-organic frameworks (MOFs) are promising materials for gas storage and separation applications due to their high tunability and porosity. The rational design of MOFs relies on accurate computational modeling, with grand canonical Monte Carlo (GCMC) simulations frequently employed to model gas uptake. However, GCMC predictions often deviate from experimental observations, limiting their utility in MOF screening. These discrepancies primarily arise from three factors: inaccuracies in the force field, neglect of atomic motions, and neglect of structural imperfections in MOFs. In this study, we systematically evaluate the impact of the first factor on the predictive accuracy of the GCMC simulations. We evaluate the widely used Universal Force Field (UFF) by comparing its predictions with experimental isotherms for four representative adsorbates, H<sub>2</sub>, CO<sub>2</sub>, C<sub>2</sub>H<sub>4</sub>, and C<sub>2</sub>H<sub>6</sub>, across 379 isotherms from 142 MOFs. The results show that UFF consistently overestimates the gas uptake in GCMC simulations. To isolate the contribution of force field inaccuracies to errors in GCMC, we developed a practical scheme for fitting force field parameters to DFT-calculated energies for a large set of MOFs. While the refined force field improves the accuracy of interatomic interaction energies, its reduction of repulsion, combined with UFF's tendency to overestimate gas uptake, ultimately amplifies the overestimation of experimental gas uptake meaurement. Our analysis suggests that improving the agreement of gas adsorption prediction with experiments requires addressing atomic motion and structural defects in MOFs alongside force field refinements.

Authors

Institutions

Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-03-18
DOI
https://doi.org/10.1021/acs.jcim.4c02044
Citations
5
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
2.59
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessing UFF and DFT-Tuned Force Fields for Predicting Experimental Isotherms of MOFs

Heather Janine Kulik, Yeongsu Cho, Jakob Teetz
5 citations
Journal of Chemical Information and Modeling
Machine Learning in Materials Science
2.59
article

Assessing UFF and DFT-Tuned Force Fields for Predicting Experimental Isotherms of MOFs

Heather Janine Kulik, Yeongsu Cho, Jakob Teetz
article en
5 citations

Abstract

Metal-organic frameworks (MOFs) are promising materials for gas storage and separation applications due to their high tunability and porosity. The rational design of MOFs relies on accurate computational modeling, with grand canonical Monte Carlo (GCMC) simulations frequently employed to model gas uptake. However, GCMC predictions often deviate from experimental observations, limiting their utility in MOF screening. These discrepancies primarily arise from three factors: inaccuracies in the force field, neglect of atomic motions, and neglect of structural imperfections in MOFs. In this study, we systematically evaluate the impact of the first factor on the predictive accuracy of the GCMC simulations. We evaluate the widely used Universal Force Field (UFF) by comparing its predictions with experimental isotherms for four representative adsorbates, H<sub>2</sub>, CO<sub>2</sub>, C<sub>2</sub>H<sub>4</sub>, and C<sub>2</sub>H<sub>6</sub>, across 379 isotherms from 142 MOFs. The results show that UFF consistently overestimates the gas uptake in GCMC simulations. To isolate the contribution of force field inaccuracies to errors in GCMC, we developed a practical scheme for fitting force field parameters to DFT-calculated energies for a large set of MOFs. While the refined force field improves the accuracy of interatomic interaction energies, its reduction of repulsion, combined with UFF's tendency to overestimate gas uptake, ultimately amplifies the overestimation of experimental gas uptake meaurement. Our analysis suggests that improving the agreement of gas adsorption prediction with experiments requires addressing atomic motion and structural defects in MOFs alongside force field refinements.

Journal of Chemical Information and ModelingVol. 65(7)
ETH Zurich (CH), Massachusetts Institute of Technology (US)
Climate action
Openalex Percentile: Top 11%
Machine Learning in Materials Science
2.59
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.