Courses Detail Information

VM571 – Machine Learning in Molecular and Materials Sciences

Instructor: Wendong Wang

Instructors (Faculty):

Credits: 3 credits

Pre-requisites: Graduate standing. Upper year undergraduate upon permission


Machine learning has an increasing impact in molecular and materials sciences. On the one hand, machine learning provides new perspectives in how we record and analyze the structures and properties of molecules and materials. On the other hand, molecular and materials sciences are fertile grounds for the application of various machine learning techniques. This course intends to be an interdisciplinary bridge between data sciences and molecular/materials sciences. You will not only learn/review neural networks, convolutional neural networks, graphs, featurization, regression and classification, and other main machine learning techniques and concepts, but also learn/review molecular conformation, chirality, protein folding, symmetry groups, electron diffraction, and other techniques and concepts in molecular and materials sciences. Some familiarity with Python is recommended but not required. We will use DeepChem as the main package for the course.

Course Topics:

Sample Syllabus