Topic outline

  • General

    Description - Project in machine learning for 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.

    "Project in machine learning for materials science" is a project-led lecture course for graduate students who wish to acquire key skills in this cross-disciplinary research field. The project work will be carried out in mixed teams (free choice of topic) and provides ideal opportunities for learning on realistic materials science datasets (experimental or computational). The target of the project work is to get hands-on experience in this field and explore the performance of different machine learning methods on different datasets, which already constitutes an important contribution to the field and the career of course participants. This course follows Introduction to Machine Learning in Materials Science, however, the previous course is not a prerequisite for this project-led course.


    3 ECT are awarded for the course. 


    The course grade is pass/fail. The passing criteria are to complete the project and to participate in the three project check-point sessions.

    Course structure and workload 

    The course is taught in Period 2

    • 2 x 2 h introduction and formation of project teams
    • 3 x 2h project check-points sessions
    • 2 x 2h project consultation sessions
    • 70h of independent project work
    Learning outcomes

    After completion of the course you will be able to:

    • Identify research questions in material science that can be solved by machine learning.
    • Understand different types of material science datasets for machine learning.
    • Perform basic data analysis of datasets.
    • Select a suitable materials science data representation as input for machine learning.
    • Consider which machine learning methods might be best for tackling different materials science problems.
    • Assess and improve the performance of machine learning models.
    • Carry out a machine learning project in materials science.
    • Critically comment on machine learning applications in materials science (on quality of data analysis, suitability of chosen methods, quality of performance assessment, etc).


    Course dates