Universität Wien

270087 VO Machine learning for molecules and materials (2022W)

4.00 ECTS (2.00 SWS), SPL 27 - Chemie


Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).


max. 24 participants
Language: German

Examination dates



Lecture and exercises will take place on Mondays from 9:30 to 12:30 in the PC pool, Währinger Str. 17, 2nd floor.
The lecture will be in English.


Aims, contents and method of the course

The basic concepts of machine learning in theoretical chemistry for molecular and materials systems will be presented. First, a general introduction of important machine learning models like neural networks and kernel-ridge regression will be provided. Python as programming language will be introduced for translating the learned concepts into computer codes. The concepts will be expanded and applied for molecular systems. Different types of representations of molecules will be discussed. Furthermore, machine learning for materials will be presented. The content will be presented in an intertwined sequence of presentations, sessions of flipped classroom and blackboard lectures. The lecture will be accompanied by computer exercises.

The goals are:
- Understanding of and overview over machine learning methods for molecular and materials systems.
- Knowledge on solving problems with machine learning codes.

Assessment and permitted materials

Performance will be assessed either by a final project or a final written test. Short tests and an oral presentation allow for bonus points. If a machine learning project is chosen, an own problem from chemistry by means of machine learning needs to be solved.

Minimum requirements and assessment criteria

Basic knowledge of theoretical chemistry (Bachelor level: Hartree-Fock, harmonic oscillator, mathematical basics, etc.) is required. The grade is defined by the final machine learning project or the written test.

Examination topics

Content of the lecture.

Reading list

- C. Bishop, Pattern recognition and machine Learning
- https://www.deeplearningbook.org/
Research articles as discussed during the oral presentations.

Association in the course directory

CH-MAT-01, PC-4, TC-3,WD3, D.4, Design

Last modified: Mo 13.03.2023 10:09