Hannes Kneiding is a PhD Candidate at the Hylleraas Centre of Excellence, Department of Chemistry, UiO. He is also part of the CompSci doctoral program. He is investigating machine learning approaches to transition metal complexes, including graph neural networks and genetic algorithms..
Transition metal complexes (TMCs) are important for applications in many different fields, ranging from cancer treatment to catalysis in industrial processes. The search for TMCs that exhibit optimal properties for their use in specific applications is inherently difficult due to the large size and high diversity of the chemical space to be explored.
We show how methods from evolutionary learning can be employed in a database-driven approach to perform efficient, multiobjective optimization of TMCs with respect to relevant molecular properties. In particular, we illustrate how the optimization can find TMCs that occupy or are very close to the Pareto front in chemical spaces of up to 1 billion members. Furthermore, we propose an algorithm to control the aim and scope of the evolution towards specific regions of the search space, and discuss possibilities of augmenting the presented approach with neural networks.
→ Read more about the seminar series in the dScience website
Programme
11:30 – Doors open and lunch is served
12:00 – "Directional Multiobjective Evolution of Metal Complexes at the Billion-Scale" by Hannes Kneiding (PhD Candidate, Hylleraas Centre)
This event is open for all PhD candidates and postdocs. No registration needed.