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Tutorial DS00—Machine Learning in Materials Science—From Basic Concepts to Active Learning

Sunday, May 8, 2022
1:30 PM - 5:00 PM
Hawaiʻi Convention Center, Level 3, Room 306B

Methods rooted in data science, machine learning, and artificial intelligence have become necessary components of materials design endeavors, being frequently applied in conjunction with both computational and experimental data. The now thriving field of materials informatics has seen the accelerated discovery of new battery materials, solar cell absorbers, thermoelectrics, and routes for autonomous synthesis and characterization. The need to educate and train the materials science workforce on the essential elements of machine learning has never been greater. This proposal aims to establish a recurring series of tutorials at MRS spring and fall meetings that introduce newcomers to all the basic concepts of machine learning in materials science, walking them through a few interactive examples that use existing datasets and ML resources.

In this tutorial, there will be short overview presentations of several key ML concepts, following which the presenter and audience will together work through Python notebooks that contain easy to-follow examples from the literature. The audience will likely constitute undergraduate and graduate students looking to get started with materials informatics, but the tutorial will be welcome and useful for any researcher. Some familiarity with writing code and making plots in Python would be useful.

Prerequisites: Basic familiarity with Python and some data science, basic background do materials science and engineering / materials design. Attendees may come prepared with an account on nanoHUB.org. The following tool will primarily be utilized for this tutorial: https://nanohub.org/tools/mrsicmsnotes.


    1:30 pm
    Introduction to ML: Supervised and Unsupervised Learning, Some High-Level Examples of ML n Materials Science
    Arun Mannodi Kanakkithodi  

    2:00 pm
    Supervised Learning Example: CSV File, Descriptors, Train Linear or RF Regression Model, Nuts and Bolts
    Arun Mannodi Kanakkithodi

    2:45 pm
    Overview of Neural Networks and Deep Learning with a Few Examples
    Saaketh Desai

    3:15 pm

    3:30 pm
    NNs for Image Datasets: CNN for Classification
    Saaketh Desai

    4:00 pm
    An Overview of Active Learning / Bayesian Optimization / Autonomous Experiments: Walk Through Using A Simple Example
    Gilad Kusne or Arun Mannodi Kanakkithodi, Saaketh Desai

    4:45 pm
    Final Session: General Discussions, Talk About Best Tools and Resources

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