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：许多材料过程与性能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（DOF）的函数。为了高效地探索这一参数空间并发现未探索区域中的能量尖点，近期建立了一种结合原子模拟与统计方法的研究技术（10.1002/adts.202100615）。本文基于主动学习算法的思想对该序贯采样技术进行了扩展，通过增加一个判断采样充分性的停止准则。为此，引入了两个可实时分析采样结果的参数：代表能量景观中最具重要性的关键区域的尖点数量，以及相邻两次迭代间的最大能量变化量。对这两个量的监测能够揭示子空间能量结构的基本特征。两者的结合为评估晶界平面倾角二维子空间（包括非周期小角晶界）的采样效果提供了必要信息。在初始设计具有合理数据点数量的条件下，仅需少量序贯迭代即可显著影响采样精度，新算法的性能优于常规的高通量采样方法。