During his PhD studies, Andreas Hedegaard Slavensky examined how atomistic global optimization algorithms could be improved. These algorithms are essential for predicting the properties of materials, as they help model materials at the atomic level. As nature tends to maximize stability of various materials, atomistic global optimization algorithms are used to determine the most stable configuration from a given number of atoms. Andreas Hedegaard Slavensky studied how different machine learning methods could be used to increase the performance of such algorithms, reducing the computational resources required to solve such optimization problems.

The PhD study was completed at Department of Physics and Astronomy, Faculty of Natural Sciences, Aarhus University.

This summary was prepared by the PhD student.

Time: Tuesday, 8 October 2024 at 13:00
Place: Building 1525, room 626, Aarhus University, Ny Munkegade 120 , 8000 Aarhus C
Title of PhD thesis: Improvements to Machine Learning-enhanced Atomistic Global Optimization Algorithms
Contact information: Andreas Hedegaard Slavensky, e-mail: aslavensky@phys.au.dk, tel.: +45 22249435

Members of the assessment committee:
Professor Hannes Jónsson, Faculty of Physical Sciences, University of Iceland, Iceland

Senior Scientist Miguel Caro, Department of Chemistry and Materials Science, Aalto University, Finland

Professor Steen Hannestad (chair), Department of Physics and Astronomy, Aarhus University, Aarhus University, Denmark

Main supervisor:
Professor Bjørk Hammer, Department of Physics and Astronomy, Aarhus University, Denmark

Language: The PhD dissertation will be defended in English

The defence is public.
The PhD thesis is available for reading at the Graduate School of Natural Sciences/GSNS, Ny Munkegade 120, building 1521, 8000 Aarhus C