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 tutorial will be part of a recurring series 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. Initiated following discussions with the MRS AI Staging Committee, this tutorial has been conducted successfully with great audience participation during the spring and fall 2022 meetings, with plans for continuation in the 2023 meetings.
There will be overview presentations of key ML concepts, following which the presenter and audience will together work through Python notebooks that contain easy-to-follow examples from the literature. Concepts will include generating and visualizing data and descriptors, training simple linear and non-linear regression models, Gaussian processes and active learning, and neural networks. 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. The audience is encouraged to run code along with the presenters, using Jupyter notebooks that can be accessed via google colab. An afternoon ML hackathon/challenge is also tentatively planned, where attendees will be given a materials dataset and have the opportunity to apply machine learning methods to real-world challenges.