Comment on ‘Physics-based representations for machine learning properties of chemical reactions’

Abstract

In a recent article in this journal, van Gerwen et al (2022 Mach. Learn.: Sci. Technol. 3 045005) presented a kernel ridge regression model to predict reaction barrier heights. Here, we comment on the utility of that model and present references and results that contradict several statements made in that article. Our primary interest is to offer a broader perspective by presenting three aspects that are essential for researchers to consider when creating models for chemical kinetics: (1) are the model’s prediction targets and associated errors sufficient for practical applications? (2) Does the model prioritize user-friendly inputs so it is practical for others to integrate into prediction workflows? (3) Does the analysis report performance on both interpolative and more challenging extrapolative data splits so users have a realistic idea of the likely errors in the model’s predictions?

Keywords

• machine learning
• kinetics
• barrier height
• graph neural network
• kernel ridge regression
• best practices