Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates. Methods for visualizing the local atomic structure, such as atom probe tomography (APT), which routinely generate datasets comprised of millions of atoms, are an important step in realizing this goal. However, state-of-the-art APT instruments generate noisy and sparse datasets that provide information about elemental type, but obscure atomic structures, thus limiting their subsequent value for materials discovery. The application of a materials fingerprinting process, a machine learning algorithm coupled with topological data analysis, provides an avenue by which here-to-fore unprecedented structural information can be extracted from an APT dataset. As a proof of concept, the material fingerprint is applied to high-entropy alloy APT datasets containing body-centered cubic (BCC) and face-centered cubic (FCC) crystal structures. A local atomic configuration centered on an arbitrary atom is assigned a topological descriptor, with which it can be characterized as a BCC or FCC lattice with near perfect accuracy, despite the inherent noise in the dataset. This successful identification of a fingerprint is a crucial first step in the development of algorithms which can extract more nuanced information, such as chemical ordering, from existing datasets of complex materials.

Program summary

Program Title: Materials Fingerprinting

CPC Library link to program files: https://doi.org/10.17632/2fhch3x85m.1

Developer's repository link: https://github.com/maroulaslab/Materials-Fingerprinting

Licensing provisions: GPLv3

Programming language: Python

Supplementary material: A user manual and examples are provided with the source code in the GitHub repository.

Nature of problem: Atom probe tomography provides sub-nanometer resolution of a material, but due to noise and sparsity introduced by the process, the crystal structure of a material cannot presently be determined from the resulting data.

Solution method: Our Materials Fingerprinting library presents a topologically informed machine learning methodology to classify the lattice structure of a material from atomic probe tomography data. We create persistence diagrams from small neighborhoods centered at each atom in the resulting APT data and use the summary statistics of a novel metric on the space of persistence diagrams as features for a classification algorithm.

中文翻译：

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材料指纹分类

利用由原子身份和三维坐标组成的实验得出的大型数据集的可用性，可以在许多类材料中取得显着进展。可视化局部原子结构的方法，例如原子探针层析成像（APT）通常会生成由数百万个原子组成的数据集，是实现这一目标的重要一步。但是，最先进的APT仪器会生成嘈杂且稀疏的数据集，这些数据集提供有关元素类型的信息，但原子结构晦涩难懂，因此限制了其随后在材料发现中的价值。材料指纹识别过程的应用，一种结合拓扑数据分析的机器学习算法，为从APT数据集​​提取迄今为止前所未有的结构信息提供了一种途径。作为概念证明，材料指纹应用于包含体心立方（BCC）和面心立方（FCC）晶体结构的高熵合金APT数据集​​。以任意原子为中心的局部原子配置被分配了拓扑描述符，尽管数据集中存在固有噪声，但可以将其表征为具有近乎完美准确性的BCC或FCC晶格。指纹的成功识别是算法开发中至关重要的第一步，该算法可以从现有的复杂材料数据集中提取更细微的信息，例如化学排序。

计划摘要

程序名称：材料指纹

CPC库链接到程序文件： https : //doi.org/10.17632/2fhch3x85m.1

开发人员的资料库链接： https : //github.com/maroulaslab/Materials-Fingerprinting

许可条款： GPLv3

编程语言： Python

补充材料： GitHub存储库中的源代码提供了一个用户手册和示例。

问题的性质：原子探针层析成像技术可提供材料的亚纳米分辨率，但由于该工艺引入的噪音和稀疏性，目前无法从所得数据确定材料的晶体结构。

解决方法：我们的材料指纹库提供了一种基于拓扑的机器学习方法，可以根据原子探针层析成像数据对材料的晶格结构进行分类。我们从所得APT数据中以每个原子为中心的小邻域中创建持久性图，并使用持久性图空间上一种新颖度量的摘要统计量作为分类算法的功能。