MACHINE LEARNING IN CHEMISTRY
An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.
Prerequisites & course information
Requisites: ( CHEM 103 , 109 , or 115 ) and ( MATH 234 , 320 , 331 , 340 , 345 , or 375 ) Course Designation: Core GenEd - Natural Science & Wellness Breadth - Physical Science Level - Intermediate L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Employ statistical and machine learning models to analyze chemical datasets. Audience: Undergraduate 2. Represent chemical compounds using molecular descriptors. Audience: Undergraduate 3. Model and predict properties and activities of chemical compounds. Audience: Undergraduate 4. Apply appropriate machine learning approaches to analyze time-series data for molecular systems. Audience: Undergraduate 5. Apply machine-learning-based potential functions to describe chemical and biological molecules. Audience: Undergraduate 6. Be familiar with programming techniques and common tools in machine learning when applied to molecular systems. Audience: Undergraduate 7. Assess the quality of machine learning approaches in the chemistry literature. Audience: Undergraduate Learning Outcomes: 1. Employ statistical and machine learning models to analyze chemical datasets. Audience: Undergraduate 2. Represent chemical compounds using molecular descriptors. Audience: Undergraduate 3. Model and predict properties and activities of chemical compounds. Audience: Undergraduate 4. Apply appropriate machine learning approaches to analyze time-series data for molecular systems. Audience: Undergraduate 5. Apply machine-learning-based potential functions to describe chemical and biological molecules. Audience: Undergraduate 6. Be familiar with programming techniques and common tools in machine learning when applied to molecular systems. Audience: Undergraduate 7. Assess the quality of machine learning approaches in the chemistry literature. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027