07-03 Diffraction glasses fireworks

This course has been completed - The Online Course was held October 17-19, 2023 from 11 a.m. to 2 p.m. EDT

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.

Course Instructor

Mathieu Bauchy_photo

Mathieu Bauchy is an Associate Professor at the University of California, Los Angeles (UCLA) and established the Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab). He received his undergraduate education in physics at Ecole Normale Supérieure (Paris, France) before pursuing a Ph.D. in condensed matter at Université Pierre et Marie Curie (Paris). He then joined the Massachusetts Institute of Technology (MIT) as a postdoctoral associate. Prof. Bauchy’s research focuses on deciphering the physics and chemistry governing disordered materials by means of simulation and machine learning. He received the Norbert J. Kreidl Award by the American Ceramics Society, the Gottardi Award from the International Commission on Glass, and the W.H. Zachariasen Award from Elsevier. He has delivered more than 150 scientific presentations and published more than 200 papers. He recently authored an open-access review paper on machine learning for glass science and engineering (see Ceramic Tech Today).

This Course has been completed, The Online Course was held October 17-19, 2023 from 11 a.m. to 2 p.m. EDT

Need to miss a session or part of a session? No problem! Each session will be video recorded and available to view on your own time for up to 60 days.


Click below register online with credit card for the conference and/or the short course. Members will be asked to log in.  Nonmembers will be prompted to create a New Visitor Registration. Download the registration form to sign up by phone, fax or mail.

If you require an invoice to facilitate payment by wire transfer, contact customer service at 1-614-890-4700 or customerservice@ceramics.org.

Non member$850
Student (member or non member)$300

If you have questions about registration, please contact Customer Service at 1-866-721-3322 (U.S. only) or 1-614-890-4700.

*Employees of ACerS Corporate Partners receive the discounted Individual Member rate.  Sapphire Corporate Partners receive an additional 20% discount; Diamond Corporate Partners receive an additional 30% discount.  Please contact Customer Service or 614-890-4700 to register employees at the discounted Corporate Partner rates.

Cancellation Policy

ACerS reserves the right to cancel a course up to 4 weeks before the scheduled presentation date. Please contact ACerS customer service at 1-866-721-3322 (U.S. only) or 1-614-890-4700 to confirm that the course is happening.