Materials Informatics

ABOUT THE COURSE:With the increasing focus on machine learning and artificial intelligence by industries that operate in the materials domain, and the enhanced digitalization efforts being taken up by several industries, this course will equip students with the necessary machine learning skills that can be applied within the materials domain. The course will not only cover the data science aspects, but also the physics behind materials modelling and computations that generate the datasets used.INTENDED AUDIENCE: Post graduate and advanced undergraduate students of Metallurgy, Materials Science and Engineering, and Ceramic Engineering disciplinesPREREQUISITES: Students of any metallurgy, materials or related disciplines are welcome.

Syllabus

Week 1: Introduction and terminologies
Week 2:Typical regression and classification workflows
Week 3:Classical models
Week 4:Perceptron and neural networks
Week 5:Convolutions and graph networks
Week 6:Density Functional Theory
Week 7:Molecular Dynamics
Week 8:Statistical Mechanics
Week 9:Lattice models and coarse graining
Week 10:Machine learned interatomic potentials: classical
Week 11:Machine learned interatomic potentials: graphs
Week 12:Advanced topics: transfer learning and generative models