We describe a local surrogate model for use in conjunction with global structure search methods. The model follows the Gaussian approximation potential (GAP) formalism and is based on a the smooth overlap of atomic positions descriptor with sparsification in terms of a reduced number of local environments using mini-batch $k$-means. The model is implemented in the Atomistic Global Optimization X framework and used as a partial replacement of the local relaxations in basin hopping structure search. The approach is shown to be robust for a wide range of atomistic system including molecules, nano-particles, surface supported clusters and surface thin films. The benefits in a structure search context of a local surrogate model are demonstrated. This includes the ability to transfer learning from smaller systems as well as the possibility to perform concurrent multi-stoichiometry searches.

翻译：我们描述了一种与全球结构搜索方法一起使用的本地替代模型,该模型遵循高西亚近似潜力(GAP)形式主义,其基础是原子位置描述符与表面稀薄胶片的平稳重叠,因为使用微型批量美元-美元-平均值的本地环境数量减少,从而导致原子位置描述符与封闭性相重叠。该模型在Atomistic Global Oppimization X框架内实施,并用作部分取代本地在流域购物结构搜索中的放松。该方法对于包括分子、纳米粒子、表面支持聚类和表面薄膜在内的多种原子系统来说是稳健的。在结构搜索环境中展示了本地替代模型的好处,其中包括从较小系统转移学习的能力,以及同时进行多科学搜索的可能性。