  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.
