ICL research group will apply probabilistic machine learning to quantum mechanics based material design

The Academy of Finland has granted funding for a 2-year project on "Probabilistic Machine Learning for Quantum Mechanics-Based Material Design". The funding is awarded to the MachQu consortium consisting of the Information, Complexity and Learning (ICL) research group lead by Assoc Prof Teemu Roos at HIIT and a team of researchers at the Department of Physics, University of Helsinki, lead by Dr Flyura Djurabekova and Prof Kai Nordlund.

The MachQu project will develop new probabilistic programming and active machine learning methods, and apply these to a currently very active topic in materials physics. It utilizes state-of-the art machine learning methods to develop new efficient quantum mechanics- based simulation tools of atomic migration processes in materials in the extreme conditions of fusion reactors and particle accelerators. The results of the materials simulations can be used for knowledge-based design of complex alloyed materials that can withstand extreme conditions.

Contact:

• Teemu Roos, Associate Professor, Department of Computer Science, University of Helsinki