Machine Learning for Functional Group Identification in Vibrational Spectroscopy: A Pedagogical Lab for Undergraduate Chemistry Students

Abstract

Techniques from the branch of artificial intelligence known as machine learning (ML) have been applied to a wide range of problems in chemistry. Nonetheless, there are very few examples of pedagogical activities to introduce ML to chemistry students in the chemistry education literature. Here we report a computational activity that introduces undergraduate physical chemistry students to ML in the context of vibrational spectroscopy. In the first part of the activity, students use ML binary classification algorithms to distinguish between carbonyl-containing and noncarbonyl-containing molecules on the basis of their infrared absorption spectra. In the second part of the activity, students test modifications to this basic analysis including different analysis parameters, different ML algorithms, and different test data sets. In a final extension of the activity, students implement a multiclass classification to predict whether carbonyl-containing molecules contain a ketone, a carboxylic acid, or another carbonyl group. This activity is designed to introduce students both to the basic workflow of a ML classification analysis and to some of the ways in which ML analyses can fail. We provide a comprehensive handout for the activity, including theoretical background and a detailed protocol, as well as data sets and code to implement the exercise in Python or Mathematica. This activity is designed as a standalone exercise for physical chemistry lab classes but can also be integrated with courses or modules on vibrational spectroscopy and computational chemistry. On the basis of student surveys, we conclude that this activity was successful in introducing students to applications of ML in chemistry.