Applications in Data and Materials Science

Applications in Data and Materials Science (ME 555)

Spring 2021: 13 weeks Jan 20-April 21

Tu/Th 10:15-11:30am.

Course Overview:

In this special topics course, AI principles will be applied to a series of materials science example problems, each taught in a module by an expert in materials science or data science.  Each module spans 2-3 weeks, demonstrating an array of data science/AI methods in unique materials case studies in advancing discovery or design principles. Each module will have a homework assignment which will include application of AI methods to the module topic. No final exam.  Pre-requisites: prior materials science course and prior AI/ML course; instructor permission.

Module 1: Boosted Decision Trees for the Discovery of Structural Patterns Controlling Bandgaps in Architected Metamaterials (Cate Brinson, Cynthia Rudin)

Module 2: Materials Optimization (Richard Sheridan, Cate Brinson)

Module 3: Machine Learning for Predicting Dynamical Heterogeneities in Materials (Gaurav Arya, Jianfeng Lu)

Module 4: Probabilistic Learning in Mechanics of Materials (Johann Guilleminot, Jianfeng Lu)

Module 5: Design of Experiments and Response Surface Methodology (David Banks)

Module 6: Artificial Intelligence Assisted Design and Synthesis of High Entropy Materials for Electrochemical Catalysis – (Jie Liu, and Stephano Curtarolo)