Affiliation:
1. Department of Chemistry Indian Institute of Technology Indore Indore 453552 India
2. Present address: SUNCAT Center for Interface Science and Catalysis Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 USA
3. SUNCAT Center for Interface Science and Catalysis SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park CA 94025 USA
Abstract
AbstractEstablishment of a scaling relation among the reaction intermediates is highly important but very much challenging on complex surfaces, such as surfaces of high entropy alloys (HEAs). Herein, we designed an interpretable machine learning (ML) approach to establish a scaling relation among CO2 reduction reaction (CO2RR) intermediates adsorbed at the same adsorption site. Local Interpretable Model‐Agnostic Explanations (LIME), Accumulated Local Effects (ALE), and Permutation Feature Importance (PFI) are used for the global and local interpretation of the utilized black box models. These methods were successfully applied through an iterative way and validated on CuCoNiZnMg and CuCoNiZnSnbased HEAs data. Finally, we successfully predicted adsorption energies of *H2CO (MAE: 0.24 eV) and *H3CO (MAE: 0.23 eV) by using the *HCO training data. Similarly, adsorption energy of *O (MAE: 0.32 eV) is also predicted from *H training data. We believe that our proposed method can shift the paradigm of state‐of‐the‐art ML in catalysis towards better interpretability.
Subject
General Chemistry,Catalysis,Organic Chemistry