Can I read Interpretable Machine‐Learning and Big Data Mining to Predict Gas Diffusivity in Metal‐Organic Frameworks on EtoBox?

Interpretable Machine‐Learning and Big Data Mining to Predict Gas Diffusivity in Metal‐Organic Frameworks by Shuya Guo; Xiaoshan Huang; Yizhen Situ; Qiuhong Huang; Kexin Guan; Jiaxin Huang; Wei Wang; Xiangning Bai; Zili Liu; Yufang Wu; Zhiwei Qiao is a Engineering article available to read on EtoBox.

What is Interpretable Machine‐Learning and Big Data Mining to Predict Gas Diffusivity in Metal‐Organic Frameworks about?

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, CH 4 , N 2 , H 2 S, O 2 , CO 2 , H 2 , and He). For these 9 gases, LGBM displays high accuracy (average R 2 = 0.962) and superior extrapolation for the diffusivity of C 2 H 6 . And this model calculation is five orders of magnitude faster than molecular dynamics (MD) simulations. Subsequently, using the trained LGBM model, an interactive desktop application is developed that can help researchers quickly and accurately calculate the diffusion of molecules in porous crystal materials. Finally, the authors find the difference in the molecular polarizability (ΔPol) is the key factor governing the diffusion selectivity by combining the trained LGBM model with the Shapley additive explanation (SHAP). By the calculation of interpretable ML, the optimal MOFs are selected for separating binary

Who reads Interpretable Machine‐Learning and Big Data Mining to Predict Gas Diffusivity in Metal‐Organic Frameworks?

Author

Shuya Guo; Xiaoshan Huang; Yizhen Situ; Qiuhong Huang; Kexin Guan; Jiaxin Huang; Wei Wang; Xiangning Bai; Zili Liu; Yufang Wu; Zhiwei Qiao

Publisher

Wiley

Published

2023

Language

EN

Field

Engineering (Physical Sciences)