Machine Learning for Glass 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 glass science & engineering and a hands-on tutorial.
Machine learning techniques are now ubiquitous in high-tech applications (e.g., search engine, face detection, spam identification, etc.) and allow computers to “learn” from existing data. More recently, machine learning methods have offered new paradigms to understand, engineer, and design glasses and materials in general. Machine learning offers a promising path to decode composition-property relationships in glasses, predict optimal glass compositions with tailored properties, pinpoint relevant structural patterns in atomistic simulations, and, more generally, guide and accelerate the design of new glasses.
This course will provide an introduction to machine learning and its application to glass science and engineering. Lectures will be complemented by a practical, hands-on tutorial on using machine learning for glass property prediction and glass 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 composition-property predictive models in glasses,
- Optimization of glass formulations with optimal properties, and,
- 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 in their research or professional activities. No prior knowledge in 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 glass 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 glass properties as a function of their composition and prescribe optimal compositions featuring tailored properties.
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 Materials Young Investigator from MDPI, and the Rising Star in Computational Materials Science Award from Elsevier. He has delivered more than 120 scientific presentations and published more than 170 papers. He recently authored an open access review paper on machine learning for glass science and engineering (see Ceramic Tech Today).
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Registration On or before August 16, 2021 After August 16, 2021
Non member $750 $850
Student (member or non member) $250 $300
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