A Route Map of Machine Learning Approaches in Heterogeneous CO₂ Reduction Reaction

Abstract

Machine learning (ML) with its indigenous predicting ability has been influential in the current scientific world and has enabled a paradigm shift in the field of CO2 reduction reaction (CO2RR). In this perspective, current research progress of ML approaches in heterogeneous electrocatalytic CO2RR has been demonstrated. The important findings related to the ML systems comprising features, output descriptors, and ML models have been summarized. Further, the opportunities and challenges in using the state-of-the-art ML methodologies along with the ways of circumventing those challenges are discussed. Finally, the interpretation of black box ML models and extensive usages of interpretable glass box and gray box models for CO2RR are encouraged for obtaining proper physical interpretations. The future directions on utilizing several such evolving ML methods to predict catalytic activity descriptors can help in a broader way to explore novel and efficient heterogeneous CO2RR and other similar catalytic reactions.

Machine learning-based screening of Mn-PNP catalysts for the CO2 reduction reaction using a region-wise ligand-encoded feature matrix

“…In this regard, machine learning (ML)-based regression techniques have emerged as a promising tool. [17][18][19] Through specific strategies, ML algorithms are able to learn underlying principles behind vast amounts of data through mathematical theory and rigorous data analysis. These laws are used to achieve the purpose of prediction.…”

Unravelling CO2 Reduction Reaction Intermediates on High Entropy Alloy Catalysts: An Interpretable Machine Learning Approach to Establish Scaling Relations

“…The CO 2 reduction reaction (CO 2 RR) is one of the most important catalytic reactions among others due to the production of carbon neutral based fuels. [1][2][3] Though, several electrocatalysts have been reported for this reaction, but Cu is the only pure catalyst known to be effective. [1,2] Nevertheless, it also suffers from poor adsorption of CO 2 and other important intermediates, and thus requires high applied potential.…”

“…[1][2][3] Though, several electrocatalysts have been reported for this reaction, but Cu is the only pure catalyst known to be effective. [1,2] Nevertheless, it also suffers from poor adsorption of CO 2 and other important intermediates, and thus requires high applied potential. Therefore, optimum adsorption of reaction intermediates is necessary according to the Sabatier principle [4] and that can be achieved through the tuning of the adsorption sites of the reaction intermediates.…”

“…Renewable energy sources are extensively demanding due to its eco‐friendly nature. The CO 2 reduction reaction (CO 2 RR) is one of the most important catalytic reactions among others due to the production of carbon neutral based fuels [1–3] . Though, several electrocatalysts have been reported for this reaction, but Cu is the only pure catalyst known to be effective [1,2] .…”

Perspective, Review, and Featured Articles of 2023

“…11 Figure 1a and Figure 1b illustrate how machine learning can predict the important intermediate energies and identify the key active sites on nanoparticles. An in-depth presentation of the key characteristics of heterogeneous catalysts for CO 2 reduction and the strengths of using ML in the research is presented by Roy et al 12 While more costly, using fully ab initio DFT approaches can greatly advance the physicochemical understanding of dopants in materials and provide a predictive model, as described by Repa and Fredin in their Perspective. 13 Understanding and optimizing the electrocatalytic production of hydrogen gas from water is a critical component of developing sustainable, renewable energy.…”