Probabilistic Machine Learning for Quantum Mechanics-Based Material Design

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.