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This exercise presents an approach that builds students’ Python coding skills alongside machine learning. Students identify regioisomerism of disubstituted benzene systems solely from infrared spectra, then collect spectra of compounds with unknown regioisomerism and predict results, generating data for future ML work.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/assignment-of-regioisomers-using-infrared-spectroscopy-a-python-coding-exercise-in-data-processing-and-machine-learning/290325/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/assignment-of-regioisomers-using-infrared-spectroscopy-a-python-coding-exercise-in-data-processing-and-machine-learning/290325.png","ImageObject",300,407,{"name":92,"@type":93},"Ava Thompson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-08","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the exercise described in the document?","Question",{"text":112,"@type":113},"To introduce machine learning alongside improving students’ general Python coding abilities by identifying regioisomerism from infrared spectra.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How do students determine regioisomerism in this exercise?",{"text":117,"@type":113},"They classify disubstituted benzene systems solely using infrared spectra, based on patterns in characteristic fingerprint features.",{"name":119,"@type":110,"acceptedAnswer":120},"What do students do at the end of the exercise?",{"text":121,"@type":113},"They collect their own spectra of compounds with unknown regioisomerism and predict the results, building a database to support future machine learning.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290325,1790122073,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46},1649267921044,"https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1786009248482753345","This article is licensed under CC-BY 4.0   \n[pubs.acs.org/jchemeduc](pubs.acs.org/jchemeduc)  Activity   \nAssignment of Regioisomers Using Infrared Spectroscopy: A Python Coding Exercise in Data Processing and Machine Learning  \nSamuel T. Cahill, * Joseph E. B. Young, Max Howe, Ryan Clark, Andrew F. Worrall, and Malcolm I. Stewart  \n Cite This: J. Chem. Educ. 2024, 101, 2925−2932  \nRead Online  \nDownloaded via 90.194.18 1.12 on December 5, 2024 at 15:36:49 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nACCESS  \n Metrics & More  \n Article Recommendations  \n*sı   \nSupporting Information  \nABSTRACT: 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  \nresults, allowing them to take ownership of their results and creating a larger database of information to draw upon for machine learning in the future.  \nKEYWORDS: Upper-Division Undergraduate, Computer-Based Learning, Chemometrics  \n■ INTRODUCTION  \nMachine learning (ML), a subset of artificial intelligence, is an increasingly used tool in chemistry research.1 Examples of ML implementations span the chemistry field from exploration of structure−activity relationships in metal−organic frameworks, electrocatalysts, perovskite photovoltaics, and polymer photocatalysts,2−5 to the development of retrosynthetic pathways and optimization of reaction conditions in organic chemistry,6,7 and  \nfurther in exploring the gas chromatography in  \napplications of Raman spectroscopy and cancer diagnoses.8−10  \nWhile machine learning techniques are not part of the core lecture content of chemistry courses at present, and are unlikely to become so in the near future, they are viable tools for exploring data-driven aspects of chemistry in practical or computational areas of an undergraduate course, particularly if the course is aimed at training students for future research careers or feeding into Master’s projects.11 With this in mind, an increasing number of exercises have been put forward to introduce the applications and processes of machine learning to undergraduate cohorts. Some recent examples of exercises introducing machine learning in this journal include:  \n• The classification of wines based on physicochemical properties.12  \n• The identification of carbonyl compounds based on infrared spectra.13  \n© 2024 The Authors. Published by American Chemical Society and Division  \n of Chemical Education, Inc. 2925  \n• Prediction of absorbance maxima of cyanine dyes based on various structural parameters.14  \n• Characterization of biodiesel samples by gas chromatography.15  \n• The determination of pH from imaged indicator paper.16  \n• Classification and determination of structural parameters of polyvinyl butyrals.17  \nEmployed in two of the examples listed above, infrared (IR) spectroscopy is a routinely used tool for the assignment of functional groups in synthetic chemistry or for direct comparison with a known spectrum to confirm the successful synthesis of a target. This technique is ubiquitous in undergraduate practical chemistry; it is, for the most part, easily interpreted and is one of the first spectroscopic techniques to which undergraduates ar","cbCaiqfYbS3evjUy","https://ap.wps.com/l/cbCaiqfYbS3evjUy","pdf",3561569,"English","# Introduction\n## Machine learning in chemistry education\n## Role of infrared (IR) spectroscopy\n## Regioisomer identification challenges\n## Exercise purpose and learning outcomes","[{\"question\":\"What is the main goal of the exercise described in the document?\",\"answer\":\"To introduce machine learning alongside improving students’ general Python coding abilities by identifying regioisomerism from infrared spectra.\"},{\"question\":\"How do students determine regioisomerism in this exercise?\",\"answer\":\"They classify disubstituted benzene systems solely using infrared spectra, based on patterns in characteristic fingerprint features.\"},{\"question\":\"What do students do at the end of the exercise?\",\"answer\":\"They collect their own spectra of compounds with unknown regioisomerism and predict the results, building a database to support future machine learning.\"}]","Assignment of Regioisomers Using Infrared Spectroscopy - A Python Coding Exercise in Data Processing and Machine Learning | PDF",1789641361]