MACHINE LEARNING IN ATOMISTIC MATERIALS SCIENCE

Level: intermediate

Language: English

Format: online on Zoom

Course duration: 15 hours (0,5 ECTS)

Target audience: Undergraduate and graduate students majoring in materials science and mechanical engineering.

Registration.

Accessibility: online lectures + self-study based on video and text materials on the platform eduportal.kau.org.ua, passing the final test;

Lecturer: Oleksandr Vasiliev, Ph.D., Leading Researcher, Associate Professor, Frantsevich Institute for Problems of Materials Science National Academy of Sciences of Ukraine, Head of Department of Applied Mathematics and Computational Experiment in Materials Science, Kyiv Academic University, Department of Applied Physics and Materials Science

The course explores the exciting intersection of machine learning (ML) and atomistic materials science, a powerful approach to materials modeling that overcomes the limitations of traditional methods such as DFT. We will review the theoretical foundations of machine learning methods and their applications using state-of-the-art tools and frameworks to analyze atomistic data, build linear atomistic models, and develop machine learning interatomic potential (MLIP) models.

Required knowledge:

basics of machine learning; familiarity with atomistic modeling of materials, in particular with density functional theory (preferable); basic Python programming

Skills acquired:

The course is free of charge. After completing the course, all participants who complete the program and pass the test will receive certificates with ECTS credits (0.5 credits).