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

现代优化材料性能的尝试越来越多地探索基于数据的研究方法。因此，例如需要关于多组分系统中的晶界的结构和能量的大量数据来用于微结构的发展。金属及合金微观组织中的晶界对材料的力学性能和功能性能有重要影响。它们可以通过溶质偏析来操纵，导致晶界能量、迁移率、结构和内聚力的变化。在这个项目中，我们通过建立一个选择性和高效的高通量计算框架来推动基于数据的材料科学的发展，通过原子模拟来确定这样的晶界能。我们将利用“实验设计”方法和在模拟晶界分布函数方面的最新进展来开发必要的算法。与传统的高通量计算相比，新的实验技术设计与非参数估计相结合，导致了对晶界参数空间最关键区域的识别和评估，并通过一系列超球谐函数表达了晶界能量。由此得到的方案可以用来提供作为函数几何和组成的晶界能作为热力学和动力学以及微观机械模型的输入。此外，这些见解将有助于更有效地规划模拟和实验，以研究其他多维、非线性关系。

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.