Topic outline

  • Description - Introduction to machine learning in materials science

    Machine learning (ML) techniques enable us to infer relationships from a large amount of seemingly uncorrelated input data. Their predictive power has made them central to product development in IT and we already use them in daily life (Amazon, Netflix, etc.). Physical sciences have been slow to capitalize on the promise of ML, even though their computational implementation is suited to modern simulation techniques. Materials science has recently benefited from a number of ML applications to materials discovery and design (featuring neural networks, genetic algorithms, regression methods, compressed sensing and Bayesian optimisation), that promise to accelerate development of novel technologies. Machine learning for materials science is an exciting new discipline that is now being taught at Aalto University.

    "Introduction to Machine learning in materials science" is a project-led lecture course for graduate students who wish to acquire key skills in this cross-disciplinary research field. Introductory lectures on materials science and machine learning will be followed by tutorial exercises. The course introduces different machine learning methods and provides examples for their application in materials science. The tutorials provide hands-on experience for the different methods. In the subsequent Project in Machine Learning for Materials Science course you will be able to apply the newly learned knowledge to your own data.

    Course level

    The course is intended for students who have completed their Bachelor's degree and have a basic understanding of machine learning or material science and a keen interest interdisciplinary science. Some programming experience or Python knowledge is required to take the course.


    3 ECT are awarded for the course. 


    The course grade is pass/fail. The passing criteria is to attend at least 5 of the 6 tutorial sessions.

    Course structure and workload 

    The course is taught in Period 1

    • 6 x 2 h lectures on machine learning in materials science
    • 6 x 2h hands-on tutorial sessions

    There is no homework for the course and no final exam.

    Learning outcomes

    After completion of the course you:

    • learned the importance of machine learning in materials science.
    • have gained an overview of different machine learning methods.
    • have hands-on experience with Python notebooks.
    • have used different machine learning methods in Python.
    • can approach a range of different problems with suitable machine learning methods.
    • can follow a presentation (e.g. conference or seminar) on machine learning in materials science.


    Course dates