Flexible force field-based screening and machine learning predictions for CO2 sorption and diffusion in MOFs
Year: 2026
Type: preprint
Abstract: Machine learning offers an efficient route for screening metal–organic frameworks (MOFs), but most existing models rely on rigid-framework simulations that neglect framework flexibility. In this work, DFT-derived flexible force fields were used to calculate CO2 sorption and self-diffusion for 5,418 MOFs, providing the training data for machine-learning models capable of rapidly predicting these properties. We investigated different ML algorithms, featurization methods, and the role of physical descriptors in predicting CO2 sorption and self-diffusion in MOFs. For CO2 sorption, a categorical boosting classifier coupled with Stoichiometric-45 featurization method used with scalar descriptors provided predictive accuracy of 76.1%. For CO2 self-diffusion, we tuned a categorical boosting regressor on revised autocorrelation functions coupled with scalar descriptors and achieved moderate predictive accuracy (R2 = 0.69). These results demonstrate that machine learning trained on properties generated from flexible force field simulations can substantially accelerate computational screening while incorporating key effects of framework flexibility, providing an efficient strategy for discovering MOFs for low-pressure CO2 capture. Summary Computational screening of 5,418 metal–organic frameworks using flexible DFT-based force fields captures framework motion effects on CO2 sorption and self-diffusion. Machine learning models were evaluated across descriptors and algorithms. A CatBoost classifier achieved 76.1% sorption accuracy, while an autocorrelation-based regressor predicted self-diffusion (R2 = 0.69), demonstrating the value of flexibility-aware modeling.
Source: ChemRxiv
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
Funder: U.S. Department of Energy
OpenAlex ID: W7223179920