Interpretable Machine-Learning and Big Data Mining to Predict Gas Diffusivity in Metal-Organic Frameworks.

ABSTRACT: For gas separation and catalysis by metal-organic frameworks (MOFs), gas diffusion has a substantial impact on the process' overall rate, so it is necessary to determine the molecular diffusion behavior within the MOFs. In this study, an interpretable machine learing (ML) model, light gradient boosting machine (LGBM), is trained to predict the molecular diffusivity and selectivity of 9 gases (Kr, Xe, CH₄ , N₂ , H₂ S, O₂ , CO₂ , H₂ , and He). For these 9 gases, LGBM displays high accuracy (average R² = 0.962) and superior extrapolation for the diffusivity of C₂ H₆ . And this model calculation is five orders of magnitude faster than molecular dynamics (MD) simulations. Subsequently, using the t

SUBMITTER: Guo S 

PROVIDER: S-EPMC10375163 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature