A Primer about Machine Learning in Catalysis – A Tutorial with Code

ChemCatChem, vol. 12, pp. 3995–4008

Abstract

Abstract Based on a well‐edited dataset from literature by Schmack et al.[1] this manuscript provides a tutorial‐like introduction to Machine Learning (ML) and Data Science (DS) based on the actual programming code in the Python programming language. The study will not only try to illustrate a ML workflow, but will also show important tasks like hyperparameter tuning and data pre‐processing which often cover much of the time of an actual study. Moreover, the study spans from classical ML methods to Deep Learning with Neural Networks.

Authors 1

1. Stefan Palkovits corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Institute for Technical and Macromolecular Chemistry Worringerweg 2 52074 Aachen Germany

   RWTH Aachen University Institute for Technical and Macromolecular Chemistry Worringerweg 2 52074 Aachen Germany