Generalized representative structures for atomistic systems.

IF 2.3 4区 物理与天体物理 Q3 PHYSICS, CONDENSED MATTER Journal of Physics: Condensed Matter Pub Date : 2024-12-09 DOI:10.1088/1361-648X/ad9791
James M Goff, Coreen Mullen, Shizhong Yang, Oleg N Starovoytov, Mitchell A Wood
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Abstract

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. the chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, along 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 here utilizing the atomic cluster expansion (ACE) basis. TheN-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. With the GRS method, 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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原子系统的广义代表结构
本文介绍了一种生成原子结构的新方法,这种结构可以再现任意材料系统、相或集合的基本特征。以前的方法可以在小型晶体结构中再现大型随机合金的基本特征(如化学无序)。利用固定晶格簇相关性来描述结构特征,就能生成随机合金的小型表征,同时还能限制晶体系统。通过使用完整的原子环境描述符集,可以对原子系统的结构特征进行更全面的描述。这些描述符可用于生成具有代表性的原子结构,而不局限于固定晶格。这里提供了一种利用原子团簇扩展(ACE)基础的通用数据驱动方法。N-body ACE 描述子是一套完整的原子环境描述子,跨越了化学和空间自由度,用于描述原子结构。其中提出的广义代表性结构(GRS)方法可生成小型原子结构,重现与任意结构和化学复杂性相对应的 ACE 描述子分布。研究表明,通过优化算法,可以高效地生成固定母晶格上的晶体系统、非晶态材料、液体和原子结构集合的系统改进表征。通过 GRS 方法,我们突出了包含类似信息量的原子机器学习训练数据集的缩减表示,以及液相的 40-72 个小原子表示。此外,我们还展示了将 GRS 方法作为新结构生成驱动力的能力。与其他数据驱动方法和仅限于高对称性系统的最先进方法相比,该方法的优势尤为突出。
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来源期刊
Journal of Physics: Condensed Matter
Journal of Physics: Condensed Matter 物理-物理:凝聚态物理
CiteScore
5.30
自引率
7.40%
发文量
1288
审稿时长
2.1 months
期刊介绍: Journal of Physics: Condensed Matter covers the whole of condensed matter physics including soft condensed matter and nanostructures. Papers may report experimental, theoretical and simulation studies. Note that papers must contain fundamental condensed matter science: papers reporting methods of materials preparation or properties of materials without novel condensed matter content will not be accepted.
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