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Intelligent prediction of materials and molecules using machine learning

PhD defence, Friday 29 November 2019, Mathias Siggaard Jørgensen.

2019.11.29 | Mie Meulengracht Christensen

Mathias Siggaard Jørgensen

During his studies, Mathias developed intelligent algorithms to predict materials and molecules at the atomic scale using machine learning. Technological breakthroughs are often triggered by novel materials and molecules with unique properties. Usually, the task of predicting these atomic-scale structures is exceedingly complex and computationally demanding.

Mathias has worked with several machine learning techniques to automate this task and provide intelligent solutions to make the task more efficient. The main contribution from his studies is an algorithm that learns, without prior knowledge or human intervention, how to build atomic-scale structures, atom by atom.

The PhD degree was completed at the Interdisciplinary Nanoscience Center (iNANO), Science and Technology, Aarhus University.

This résumé was prepared by the PhD student.





Time: Friday, 29 November 2019 at 13:15
Place: Building 1593, room 012, iNANO Auditorium, iNANO, Aarhus University
Title of PhD thesis: Machine learning in atomistic structure prediction
Contact information: Mathias Siggaard Jørgensen, e-mail: mj@inano.au.dk, Phone: +45 4046 7097

Members of the assessment committee:

Dr. Luca Ghiringhelli, Theory Department, Fritz Haber Institute, Germany
Research Scientist James Kirckpatrick, Deepmind, Google, United Kingdom
Associate Professor Victoria Birkedal (chair), iNANO, Aarhus University, Denmark
Main supervisor:
Professor Bjørk Hammer, iNANO and Department of Physics and Astronomy, Aarhus University

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 Science and Technology/GSST, Ny Munkegade 120, building 1521, 8000 Aarhus C.

PhD defence
12183 / i43