Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


After completion of the course you will:

  • Have deepened your understanding of machine learning (ML) in materials science (MS)
  • Be able to carry out a project in ML for MS
  • Understand different types of MS datasets for ML
  • Perform basic data analysis of datasets
  • Assess and improve the performance of ML models
  • Select a suitable MS data representation as input for ML
  • Critically comment on ML applications in MS (on quality of data analysis, suitability of chosen ML method, quality of assessment of ML performance, etc).

Credits: 3

Schedule: 24.10.2023 - 28.11.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Matthias Stosiek, Armi Tiihonen, Patrick Rinke

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English


  • valid for whole curriculum period:

    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.

    The "Project in Machine learning in materials science" complements course PHYS-E0549 Introduction to Machine Learning in Materials Science. It is a project-led 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 ML methods on different datasets, which already constitutes an important contribution to the field and the career of course participants.

Assessment Methods and Criteria
  • valid for whole curriculum period:

    The course grade is pass/fail. Attendance of the contact sessions and completion of the project is a prerequisite for passing the course.

  • valid for whole curriculum period:

    • 3 x 1h introduction contact sessions
    • project work in small teams
    • 3 x 2h project checkpoint contact sessions

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


Substitutes for Courses
SDG: Sustainable Development Goals

    7 Affordable and Clean Energy

    12 Responsible Production and Consumption

    13 Climate Action


Further Information
  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Autumn II
    2023-2024 Autumn II