Materials Fingerprinting Classification

TL;DR

This paper introduces a machine learning and topological data analysis-based materials fingerprinting method that accurately classifies atomic structures in noisy, sparse datasets from atom probe tomography, aiding materials discovery.

Contribution

It presents a novel fingerprinting approach combining machine learning and topology to classify atomic structures in noisy datasets, advancing materials analysis capabilities.

Findings

Accurately classifies BCC and FCC structures despite noise

Demonstrates potential for extracting detailed structural information

Provides a foundation for analyzing complex material datasets

Abstract

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.