Machine Learning for Materials ScienceDescriptionMachine learning has received a lot of hype over the last decade, with techniques such as convolutional neural networks and deep learning powering a new generation of datadriven analytics. On the other hand, material science has benefited a lot from largescale modeling & simulation through Molecular Dynamics, Density Functional Theory, and Differential Equations describing rigorous scientific laws. This course aims to provide students trainings with a convergence of the two disciplines. We will start from machine learning basics, its mathematical foundations, then move on to modern machine learning methods for material science problems and handson study with Python. Particularly, students will learn about how to combine the datadriven ML techniques with existing knowledge of material science to give reliable physical predictions. Various case studies will be discussed, with realworld material science applications. Lectures2022 Spring: Tuesday and Thursday, 3:00–4:30pm, remotely via Canvas > Zoom Lecture Notes (available upon request)
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