Comment on ‘Physics-based representations for machine learning properties of chemical reactions’
Name
Spiekermann_2023_Mach._Learn.__Sci._Technol._4_048001.pdf
Description
Published version
Size
456.99 KB
Format
Adobe PDF
Checksum (MD5)
e4824ab8d4adf5cef1229dbc549f2724
Author(s)
Spiekermann, Kevin A
Stuyver, Thijs
Pattanaik, Lagnajit
Green, William H
Date Issued
October 6, 2023
Journal
Machine Learning: Science and Technology
Publisher
IOP Publishing
Citation
Kevin A Spiekermann et al 2023 Mach. Learn.: Sci. Technol. 4 048001.
Version
Final published version
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?
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1088/2632-2153/acee42
