LEARNING OUTCOMES
After completion of the course you will be able to:
- Have knowledge of different ML methods
- Have practical experience of applying ML methods to materials science examples
- Identify research questions in material science (MS) that can be solved by machine learning (ML)
- Consider which ML methods might be best for tackling different MS problems
Credits: 3
Schedule: 04.09.2023 - 10.10.2023
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): 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
CONTENT, ASSESSMENT AND WORKLOAD
Content
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.
"An Introduction to Machine learning in materials science" is a lecture and tutorial based course for graduate students who wish to acquire key skills in this cross-disciplinary research field. Lectures will introduce different machine learning methods and demonstrate their application in materials science. The lectures will be complemented by tutorial exercises that illustrate the use of machine learning methods on materials science data sets.
Assessment Methods and Criteria
valid for whole curriculum period:
The course grade is pass/fail. Attendance in the tutorials and successful completion of short learning quizes is a prerequisite for passing the course.
Workload
valid for whole curriculum period:
- 12 x 1 h introductory 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.
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
7 Affordable and Clean Energy
12 Responsible Production and Consumption
13 Climate Action
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Autumn I
2023-2024 Autumn I