Assignment of Regioisomers Using Infrared Spectroscopy: A Python Coding Exercise in Data Processing and Machine Learning

Samuel Cahill; Joseph E. B. Young; Max Howe; Ryan Clark; Andrew F. Worrall; Malcolm I. Stewart

doi:10.1021/acs.jchemed.4c00295

Assignment of Regioisomers Using Infrared Spectroscopy: A Python Coding Exercise in Data Processing and Machine Learning

Samuel Cahill^*, Joseph E. B. Young, Max Howe, Ryan Clark, Andrew F. Worrall, Malcolm I. Stewart

^*Corresponding author for this work

Chemistry

Research output: Contribution to journal › Article › peer-review

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Abstract

Machine learning is a set of tools that are increasingly used in the field of chemistry. The introduction of potential uses of machine learning to undergraduate chemistry students should help to increase their comprehension of and interest in machine learning processes and can help support them in their transition into graduate research and industrial environments that use such tools. Herein we present an exercise aimed at introducing machine learning alongside improving students’ general Python coding abilities. The exercise aims to identify the regioisomerism of disubstituted benzene systems solely from infrared spectra, a simple and ubiquitous undergraduate technique. The exercise culminates in students collecting their own spectra of compounds with unknown regioisomerism and predicting the results, allowing them to take ownership of their results and creating a larger database of information to draw upon for machine learning in the future.

Original language	English
Pages (from-to)	2925-2932
Number of pages	8
Journal	JOURNAL OF CHEMICAL EDUCATION
Volume	101
Issue number	7
Early online date	19 Jun 2024
DOIs	https://doi.org/10.1021/acs.jchemed.4c00295
Publication status	Published - 19 Jun 2024

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title = "Assignment of Regioisomers Using Infrared Spectroscopy: A Python Coding Exercise in Data Processing and Machine Learning",

abstract = "Machine learning is a set of tools that are increasingly used in the field of chemistry. The introduction of potential uses of machine learning to undergraduate chemistry students should help to increase their comprehension of and interest in machine learning processes and can help support them in their transition into graduate research and industrial environments that use such tools. Herein we present an exercise aimed at introducing machine learning alongside improving students{\textquoteright} general Python coding abilities. The exercise aims to identify the regioisomerism of disubstituted benzene systems solely from infrared spectra, a simple and ubiquitous undergraduate technique. The exercise culminates in students collecting their own spectra of compounds with unknown regioisomerism and predicting the results, allowing them to take ownership of their results and creating a larger database of information to draw upon for machine learning in the future.",

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AU - Stewart, Malcolm I.

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Assignment of Regioisomers Using Infrared Spectroscopy: A Python Coding Exercise in Data Processing and Machine Learning

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