Abstract: In materials research, we have learnt to predict the evolution of microstructure starting with the atomic level processes. We know about defects — point and extended, — and we know that these can be crucial for the final structural (and related mechanical and electrical) properties. Often simple macroscopic differential equations, which are used for the purpose, fail to predict simple changes in materials. Many questions remain unanswered. Why a ductile material suddenly becomes brittle? Why a strong concrete bridge suddenly cracks and eventually collapses after serving for tens of years? Why the wall of high quality steels in fission reactors suddenly crack? Or, why the clean smooth surface roughens under applied electric fields? All these questions can be answered, if one peeks in to atom’s behavior imagining it jumping inside the material. But how the atoms “choose” where to jump amongst the numerous possibilities in complex metals? Tedious parameterization can help to deal with the problem, but machine learning can provide a better and more elegant solution to this problem.

In my presentation, I will explain the problem at hand and show a few examples of former and current application of Neural Network for calculating the barriers for atomic jumps with the analysis of how well the applied NN worked.

Speaker: Flyura Djurabekova

Affiliation: Department of Physics, University of Helsinki

Place of Seminar: University of Helsinki