Структура за темами

  • Course Info

    Course name

    Machine Learning in Materials Lifecycle






    None, but students with a good background in mathematics, physics, and chemistry, as well as basic understanding of materials science and engineering, will find the course easier to follow.


    Students will acquire basic understanding of how machine learning models are applied in Materials Science and Engineering, their possibilities and limitations, skills necessary to get a general understanding of specialized literature on the topic, understanding of applicability of certain machine learning algorithms to specific problems, ability to participate in discussions on the matters of Machine Learning application to materials related problems.


    8 hours


    Students, professionals, and executives/managers in the fields of general Machine Learning, experimental Materials Science and Engineering, non-ML computational Materials Science.


    Picture of the lector

    Oleksandr Vasiliev, Ph.D., Leading Researcher, Associate Professor

    Frantsevich Institute for Problems of Materials Science National Academy of Sciences of Ukraine, Group of Atomistic Modeling and Machine Learning in Materials Science

    Kyiv Academic University, Department of Applied Physics and Materials Science


    The course was developed within the framework of the Project BOOSTalent: Empowering HEIs to Lead in Deep Tech Excellence with Innovative AI and ML for Sustainability, Aerospace, Advanced Materials, and Electronics (EIT HEI Initiative).