LEARNING OUTCOMES
After completion of the course you will be able to:
- Identify research questions in material science (MS) that can be solved by machine learning (ML)
- Understand different types of MS datasets for ML
- Perform basic data analysis of datasets
- Select a suitable MS data representation as input for ML
- Consider which ML methods might be best for tackling different MS problems
- Assess and improve the performance of the ML model
- Carry out a computational project on ML for MS
- 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: 5
Schedule: 13.09.2021 - 10.12.2021
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Patrick Rinke, Milica Todorovic
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.
"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, student-led seminars and a light research project. The research 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. It is possible to take the course with two levels of difficulty:
- level 1: attending classes and tutorials, surveying the field and giving a seminar (pass with 3 ECT). Attendance: classes, tutorials, seminars: can miss 2 out of 8 sessions
- level 2: level 1, with seminar work replaced by participating in the project work (pass with 5 ECT). Attendance: classes, tutorials, seminars, project presentations: can miss 2 out of 11 sessions
Workload
valid for whole curriculum period:
- 4 x 2 h introductory lectures on machine learning in materials science
- 2 x 4h hands-on tutorial sessions
- 3 x 2h student seminar presentations
- project work in small teams
- 3 x 2h project checkpoint contact sessions
There is no homework for the course and no final exam.
DETAILS
Study Material
valid for whole curriculum period:
Textbooks
Modelling for Materials Science is a relatively new subject at universities. An excellent textbook that provides a general overview and introduction to different techniques is: Atomistic Computer Simulations - A Practical Guide, by V. Brazdova and D. R. Bowler.
Good introduction books to machine learning are: Introduction to Statistical Learning (with applications in R), by G. James, D. Witten, T. Hastie, and R. Tibshirani; Pattern Recognition and Machine Learning by C. Bishop.
Research articles: general
Nature Physics Editorial, "Machine learning: New tool in the box", Lenka Zdeborová
Nature Physics 13, 420 421 (2017) - fundamental materials science applicationsTutorial article, "Machine learning for quantum mechanics in a nutshell", M. Rupp, Int. J. Quantum Chem. 2015, 115, 1058 1073 (in
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
3 Good Health and Well-being
7 Affordable and Clean Energy
9 Industry, Innovation and Infrastructure
10 Reduced Inequality
13 Climate Action
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Period:
2020-2021 Autumn I-II
2021-2022 Autumn I-II
Course Homepage: https://mycourses.aalto.fi/course/search.php?search=PHYS-E0547
Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.
WebOodi