Universität Wien

270095 UE Machine learning for molecules and materials (2022W)

2.00 ECTS (2.00 SWS), SPL 27 - Chemie
Continuous assessment of course work


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



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


Aims, contents and method of the course

The basic concepts of machine learning in theoretical chemistry for molecules and materials are presented. First, a general introduction to important machine learning models such as neural networks and kernel ridge regression is given. Python as a programming language is introduced to translate the concepts learned into computer codes. The concepts are extended and applied to molecular systems. Different types of representations of molecules are discussed. In addition, machine learning for materials is introduced. The content is presented in the form of computer exercises and is only useful in conjunction with the associated lecture.

The objectives are:
- Understanding and overview of machine learning methods for molecules and materials.
- Ability to write small programs in Python, with an emphasis on machine learning for theoretical chemistry.
- Knowledge of solving problems with machine learning.

Assessment and permitted materials

Performance will be assessed through participation in the computer exercises. There is the possibility to prove the performance by solving an own problem from chemistry by means of machine learning.

Minimum requirements and assessment criteria

Basic knowledge of theoretical chemistry (bachelor level: Hartree-Fock, harmonic oscillator, basic mathematics, etc.) is assumed. The grade is composed of the averaged results of the various computer exercises and the participation in the exercises.

Examination topics

Content of the course.

Reading list

- C. Bishop, Pattern recognition and machine Learning
- https://www.deeplearningbook.org/

Association in the course directory

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

Last modified: Mo 13.03.2023 10:09