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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.

Course duration: 15 hours (0,5 ECTS)

Start: from 14.05.2024 (online lectures in ZOOM, access to materials on the platform eduportal.kau.org.ua)

Lecture schedule: 14 - 17 May, daily at 15:00

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