The traditional viewpoint of plastic deformation of crystalline solids is that of a smooth flow-like process. Nevertheless, novel, more precise experiments have demonstrated that especially when the sample size approaches the scale of a few microns, the deformation process consists of a sequence of discrete strain bursts of widely varying sizes. Trying to understand the statistical properties and origin of these bursts is one of the hot topics of contemporary non-equilibrium physics. On the other hand, for applications it is important to be able to predict and control details of the deformation process. In this project we study via computer simulations the fundamental nature of the bursty plastic deformation process of micron-scale crystals, and develop methods based on machine learning to predict and optimise the deformation process for applications. The project is anticipated to have impact on both fundamental science as well as for applications.
Academy of Finland