Explore the intersection of computational materials science and machine learning with the From First Principles to Machine Learning Methods in Materials Informatics course. This advanced, self-paced program is ideal for researchers, engineers, and graduate students specializing in materials engineering and atomistic simulations who wish to enhance their understanding of machine learning applications in their field.
The course provides a structured pathway from foundational principles to practical implementation, focusing on creating and applying machine learning models for atomistic simulations. Participants will learn to select suitable models, prepare first-principles data, and effectively train, validate, and deploy machine learning models tailored for materials informatics.
Delivered in English, the 15-hour course includes 6 hours of engaging lectures. Led by Dr. Oleksandr Vasiliev, a leading researcher and associate professor with extensive expertise in computational materials science, the course blends theoretical depth with practical insights.
A strong foundation in machine learning, density functional theory, and Python programming is recommended. Upon successful completion of the program and assessments, participants will receive a certificate with 0.5 ECTS credits.
This free course is a valuable opportunity to advance your skills at the cutting edge of materials science and machine learning. Register now to begin your journey in materials informatics!
Join the course to see full details! The course is completely free.
