原子系统的广义代表性结构
Generalized Representative Structures for Atomistic Systems
AI总结：
本文提出广义代表性结构（GRS）方法，利用原子簇展开（ACE）基生成不受固定晶格限制的小型原子结构，高效重现任意材料系统的结构与化学特征，在数据集精简与新颖结构生成上优于现有方法。
摘要：
本文提出了一种新方法，用于生成能够重现任意材料系统、相或系综本质特征的原子结构。以往的方法允许在小晶体结构中重现大型随机合金的本质特征（例如化学无序）。生成随机合金的小型表示的能力仅限于晶体系统，这源于使用固定晶格的簇关联来描述结构特征。通过使用完整的原子环境描述符集合，获得了原子系统结构特征的更一般性描述。这些描述符被用于生成不受固定晶格限制的代表性原子结构。本文提供了一种利用原子簇展开（ACE）基的通用数据驱动方法。N体ACE描述符是完整的原子环境描述符集合，跨越化学和空间自由度，被用于描述原子结构。本文提出的广义代表性结构（GRS）方法生成小型原子结构，这些结构能够重现对应于任意结构和化学复杂性的ACE描述符分布。结果表明，通过优化算法，可以高效地生成固定母晶格上的晶体系统、非晶材料、液体以及原子结构系综的系统性可改进表示。我们重点介绍了包含相似信息量的原子机器学习训练数据集的精简表示，以及液相的40-72个原子的小型表示。还展示了使用GRS方法作为驱动知情新颖结构生成的能力。突出了相对于其他数据驱动方法和限于高对称性系统的最先进方法的优势。
英文摘要：
A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g. chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided utilizing the atomic cluster expansion(ACE) basis. The N-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure(GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. We highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40-72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted.
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