Efficient sampling and representation of the grain boundary geometry and composition space: atomistic simulation meets statistical methodology

点击翻译按钮获取中文摘要

Modern attempts to optimise materials’ properties increasingly explore data based research approaches. As a consequence, there is for instance a demand of extensive data on structure and energies of grain boundaries in multicomponent systems for microstructure development. Grain boundaries in microstructures of metals and alloys have a significant influence on the material’s mechanical and functional properties. They can be manipulated via solute segregation, leading to changes in grain boundary energy, mobility, structure, and cohesion. In this project we advance data based materials science by establishing a selective and efficient high-throughput computational framework to determine such grain boundary energies via atomistic simulations. We will develop the necessary algorithms by making use of “design of experiment” approaches and recent developments in modelling grain boundary distribution functions. In contrast to conventional high-throughput calculations, the new design of experiment techniques in combination with nonparametric estimation leads to an identification and assessment of the most critical regions of the grain boundary parameter space and an expression of the grain boundary energy by a series of hyperspherical harmonics. The resulting scheme can be used to provide grain boundary energies as a function geometry and composition as input for thermodynamic and kinetic, as well as micromechanical modelling. Furthermore, the insights will contribute to a more efficient planning of simulations and experiments for investigations of other multi-dimensional, non-linear relationships as well.