Machine Learning for Materials Science and Engineering

Instructor: Mathieu Bauchy, University of California, Los Angeles (UCLA) 

This 3 day (9 hour) course will offer an introduction to machine learning applied to materials science & engineering and a hands-on tutorial.

Course description

Machine learning techniques are now ubiquitous in high-tech applications (e.g., ChatGPT, search engine, face detection, etc.) and allow computers to “learn” from data to perform tasks that are typically accomplished by humans. More recently, machine learning methods have offered new paradigms to understand, engineer, and design materials. Machine learning offers a promising path to decode composition-structure-property relationships, predict optimal compositions with tailored properties, identify optimal processing conditions, and, more generally, guide and accelerate the design of new materials with desirable properties and functionalities.

This course will provide an introduction to machine learning and its application to materials science and engineering. Lectures will be complemented by a practical, hands-on tutorial on using machine learning for materials property prediction and optimization. Topics covered will include:

• General introduction to supervised and unsupervised machine learning
• Review of existing machine learning methods and applications
• Overview of a complete machine learning pipeline: data collection, data cleaning, feature engineering, learning algorithm selection and training, hyperparameter optimization, testing and deployment
• Development of predictive models
• Inverse design of materials
• Integrations of machine learning, simulations, and experiments

Who should attend

This introductory course is targeted to students, scientists, or engineers who are interested in incorporating machine learning into their research or professional activities. No prior knowledge of machine learning or computer science is expected, although certain aspects of this course will also be relevant to individuals who are already familiar with machine learning. By the end of this class, participants are expected to:

• Understand the possibilities and limitations of machine learning,
• Be familiar with the different applications of machine learning in the field of materials science and engineering,
• Be able to choose the right machine learning method to solve a given problem,
• Have the necessary introductory theoretical background to understand previous studies focusing on machine learning and material informatics, and,
• Implement a machine learning model to predict material properties as a function of their composition/structure and prescribe optimal materials featuring tailored properties.