Transition States Energies from Machine Learning: An Application to Reverse Water-Gas Shift on Single-Atom Alloys
This work addresses the problem of high computational cost in catalyst screening for materials scientists and chemists, offering a robust framework for future catalyst design, though it is incremental as it builds on existing ML and DFT methods.
The authors tackled the bottleneck of accurately predicting transition state energies for catalyst screening by developing a machine learning model based on Gaussian process regression with a graph kernel, which reduced errors in turnover frequency predictions by almost an order of magnitude compared to traditional scaling relations. They applied this model to the reverse water-gas shift reaction on single-atom alloys, identifying promising catalysts and demonstrating improved accuracy.
Obtaining accurate transition state (TS) energies is a bottleneck in computational screening of complex materials and reaction networks due to the high cost of TS search methods and first-principles methods such as density functional theory (DFT). Here we propose a machine learning (ML) model for predicting TS energies based on Gaussian process regression with the Wasserstein Weisfeiler-Lehman graph kernel (WWL-GPR). Applying the model to predict adsorption and TS energies for the reverse water-gas shift (RWGS) reaction on single-atom alloy (SAA) catalysts, we show that it can significantly improve the accuracy compared to traditional approaches based on scaling relations or ML models without a graph representation. Further benefitting from the low cost of model training, we train an ensemble of WWL-GPR models to obtain uncertainties through subsampling of the training data and show how these uncertainties propagate to turnover frequency (TOF) predictions through the construction of an ensemble of microkinetic models. Comparing the errors in model-based vs DFT-based TOF predictions, we show that the WWL-GPR model reduces errors by almost an order of magnitude compared to scaling relations. This demonstrates the critical impact of accurate energy predictions on catalytic activity estimation. Finally, we apply our model to screen new materials, identifying promising catalysts for RWGS. This work highlights the power of combining advanced ML techniques with DFT and microkinetic modeling for screening catalysts for complex reactions like RWGS, providing a robust framework for future catalyst design.