Materials Fingerprinting Classification
Materials Fingerprinting Classification
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DOI：
10.1016/j.cpc.2021.108019
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Adam Spannaus;K. Law;P. Luszczek;Farzana Nasrin;Cassie Putman Micucci;P. Liaw;L. Santodonato;D. Keffer;V. Maroulas
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作者：
Adam Spannaus;K. Law;P. Luszczek;Farzana Nasrin;Cassie Putman Micucci;P. Liaw;L. Santodonato;D. Keffer;V. Maroulas
随着实验得出的由原子身份和三维坐标组成的大型数据集的可获得性，可以在许多类别的材料方面取得重大进展。可视化局部原子结构的方法，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。然而，最先进的APT仪器产生的数据集嘈杂而稀疏，提供了关于元素类型的信息，但原子结构模糊，从而限制了它们随后对材料发现的价值。材料指纹分析过程是一种结合了拓扑数据分析的机器学习算法，它的应用为从APT数据集中提取前所未有的结构信息提供了一条途径。作为概念验证，材料指纹被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。为以任意原子为中心的局域原子构型分配了一个拓扑描述符，利用该拓扑描述符，尽管数据集中存在固有的噪声，但它可以近乎完美的精度被表征为BCC或FCC晶格。这种指纹的成功识别是开发算法的关键的第一步，该算法可以从复杂材料的现有数据集中提取更细微的信息，例如化学排序。程序概述程序标题：材料指纹打印CPC库链接到程序files:https://doi.org/10.17632/2fhch3x85m.1Developer‘s库link:https://github.com/maroulaslab/Materials-FingerprintingLicensing规定：GPLv3编程语言：Python补充材料：用户手册和示例随GitHub库中的源代码一起提供。问题的性质：原子探针断层扫描提供材料的亚纳米分辨率，但由于该过程引入的噪声和稀疏性，解决方法：我们的材料指纹库提供了一种拓扑信息机器学习方法，可以从原子探针断层扫描数据中对材料的晶格结构进行分类。我们从生成的APT数据中以每个原子为中心的小邻域创建持久图，并使用持久图空间上的一种新度量的汇总统计作为分类算法的特征。
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 summaryProgram Title:Materials FingerprintingCPC Library link to program files:https://doi.org/10.17632/2fhch3x85m.1Developer's repository link:https://github.com/maroulaslab/Materials-FingerprintingLicensing provisions:GPLv3Programming language:PythonSupplementary 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.