The Disputation will be live streamed for everyone else.
The livestream will be activated 15 minutes before the Defense starts.
Trial lecture
September 27th, 09:15 AM, Auditorium 4, Chemistry building
Trial lecture title:
"Large language models for chemistry - passing fad or transformative tool?"
Kreeringssammendrag/Conferral summary
Overgangsmetallkomplekser er avgjørende for en rekke bruksområder, blant annet nanoporøse materialer, fornybar energi og medisinsk kjemi. Optimaliseringen av dem med hensyn til bestemte egenskaper kan fremskyndes drastisk ved å identifisere lovende kandidater in silico før man validerer treffene med eksperimenter. I denne avhandlingen presenteres fire ulike maskinlæringsmetoder som er spesielt utviklet for bruk på overgangsmetallkomplekser.
Main research findings
Transition metal complexes (TMCs) are essential for a large range of different applications in renewable energies, energy storage, medicinal chemistry and more. Finding TMCs that have optimal properties for such applications is an expensive and time-consuming task as it traditionally requires experiments. Computer-based methods can accelerate this process by screening a large number of TMCs for promising candidates beforehand. Particularly machine learning approaches that leverage existing data have recently drawn a lot of attention. However, most research in this field focuses on small organic molecules which are easier to handle due to their simpler chemistry.
In this thesis I present four different and novel machine learning approaches, specifically designed for their application to TMCs. In particular they are: (1) a neural network leveraging quantum chemistry informed graph representations, (2) a genetic algorithm performing directional and multi-objective optimization, (3) a set of autocorrelation descriptors capturing bond-bond and atom-bond relationships, and (4) a generative neural network for the generation of novel ligands with specific properties. In addition to these methods I also present two TMC datasets and discuss how they can be exploited for the computer-aided discovery of TMCs.
Candidate contact information
LinkedIn: www.linkedin.com/in/hkneiding
Github: https://github.com/hkneiding
E-post: hanneskn@uio.no
Tel.: +49 173 9178197
Adjudication committee
Professor Kim Jelfs
Imperial College London, United Kingdom
Professor Kjell Jorner
ETH Zürich, Switzerland
Professor Reidar Lund
Department of Chemistry, University of Oslo, Norway
Supervisors
Senior Researcher David Balcells
Department of Chemistry, University of Oslo, Norway
Associate Professor Riccardo De Bin
Department of Chemistry, University of Oslo, Norway
Professor Thomas Bondo Pedersen
Department of Chemistry, University of Oslo, Norway
Chair of defence
Professor Truls Norby
Department of Chemistry, University of Oslo, Norway