Many materials processes and properties depend on the anisotropy of the energy of grain boundaries, i.e. on the fact that this energy is a function of the five geometric degrees of freedom (DOF) of the grain boundaries. To access this parameter space in an efficient way and discover energy cusps in unexplored regions, a method was recently established, which combines atomistic simulations with statistical methods 10.1002/adts.202100615. This sequential sampling technique is now extended in the spirit of an active learning algorithm by adding a criterion to decide when the sampling is advanced enough to stop. To this instance, two parameters to analyse the sampling results on the fly are introduced: the number of cusps, which correspond to the most interesting and important regions of the energy landscape, and the maximum change of energy between two sequential iterations. Monitoring these two quantities provides valuable insight into how the subspaces are energetically structured. The combination of both parameters provides the necessary information to evaluate the sampling of the 2D subspaces of grain boundary plane inclinations of even non-periodic, low angle grain boundaries. With a reasonable number of datapoints in the initial design, only a few sequential iterations already influence the accuracy of the sampling substantially and the new algorithm outperforms regular high-throughput sampling.

翻译：许多材料过程与性质取决于晶界能量的各向异性，即该能量是晶界五个几何自由度函数的特性。为高效探索该参数空间并发现未探明区域的能量尖点，近期建立了一种结合原子模拟与统计方法的技术（10.1002/adts.202100615）。本研究基于主动学习算法思想对该顺序采样技术进行扩展，通过增加一个判据以确定采样何时达到可停止的充分程度。为此，引入两个实时分析采样结果的参数：代表能量景观中最具关注度与重要区域的尖点数量，以及连续两次迭代间的最大能量变化。监测这两个参量可深入揭示子空间能量结构的特征。二者结合能为评估晶界平面倾角二维子空间（包括非周期低角度晶界）的采样效果提供必要信息。在初始设计包含合理数据点的情况下，仅需数次顺序迭代即可显著影响采样精度，且新算法性能优于常规高通量采样。