.png){kind=icon}
.png){kind=icon}
Return
Materials Fingerprinting Classification
.png){kind=icon}
OA
AI
DOI：10.1016/j.cpc.2021.108019.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Significant progress in many classes of materials could be made with the availability of experimentallyderived 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-ofthe-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
Keywords:
Atom probe tomography
High entropy alloy
Machine learning
Topological data analysis
Materials discovery
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.4
Papers:
1.2W
Citations:
3.7W
View more organizations