A Primer about Machine Learning in Catalysis – A Tutorial with Code. Issue 16 (5th May 2020)

• Record Type: Journal Article
• Authors: Palkovits, Stefan
• Is Part Of: ChemCatChem. Volume 12:Issue 16(2020)
• Page Start: 3995
• Page End: 4008
• Publication Date: 2020-05-05
• Subjects: Data Science -- Machine Learning -- Catalysis -- Deep Learning; Catalysis -- Periodicals; 541.39505
• DOI: 10.1002/cctc.202000234
• Languages: English
• ISSNs: 1867-3880

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. Abstract : Machine Learning : In a tutorial style article with programming code a Machine Learning workflow will be illustrated based on literature known data for the oxidative coupling of methane.