Theory-Guided Machine Learning to Predict the Performance of Noble Metal Catalysts in the Water-Gas Shift Reaction

Machine learning (ML) has widespread applications in catalyst discovery and reaction optimization. We present a theory-guided machine learning framework to evaluate the carbon monoxide (CO) conversion performance of noble metal catalysts in water-gas shift (WGS) reaction. Our study is based on an open source WGS dataset, which we modify significantly to be consistent with the chemical reaction principles. We apply state-of-the-art ML models including artificial neural networks, extreme gradient boosting to predict CO conversion percentage. These models show superior regression performance than the previously reported results in the literature. We further generalize the existing data structure by including physical, chemical and surface chemistry properties as fingerprint features that rationalize the importance of all the input features for CO conversion. We noticed that purely data-driven ML models frequently violate the thermodynamic equilibrium principle and predict unphysical CO conversion percentage. We address these two problems by developing a custom loss function and an additional activation function in our neural networks architecture. Our proposed theory-guided ML model displays high accuracy (R-2 score is 0.95 and root mean square error is 6.87) and physically robust predictions. The model also opens up promising possibilities to improve CO conversion percentage, which were previously unexplored in experiments.

Automated summary

This study presents a theory-guided machine learning framework for evaluating the CO conversion performance of noble metal catalysts in the WGS reaction. By using advanced ML models and incorporating multiple features, the accuracy of the predictions is significantly improved. The proposed model addresses the limitations of purely data-driven models and opens up new possibilities for improving CO conversion percentage.