Cluster characterization in atom probe tomography: Machine learning using multiple summary functions

Bennett, Roland A.; Proudian, Andrew P.; Zimmerman, Jeramy D.

doi:10.1016/j.ultramic.2023.113687

Ultramicroscopy

2023

DOI: 10.1016/j.ultramic.2023.113687

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Cluster characterization in atom probe tomography: Machine learning using multiple summary functions

Roland A. Bennett

Andrew P. Proudian

Jeramy D. Zimmerman

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2024

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Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Ripley's function is a popular method for point pattern analysis(Bennett et al, 2023). It is a cumulative functionthat describes the distribution of distances within a point pattern.…”

mentioning

confidence: 99%

A data‐driven adaptive geospatial hotspot detection approach in smart cities

Yan,

Quan,

Wang

2024

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Hotspot detection from geo‐referenced urban data is critical for smart city research, such as traffic management and policy making. However, the classical clustering or classification approach for hotspot detection mainly aims at identifying “hotspot areas” rather than specific points, and the setting of global parameters such as search bandwidth can lead to inaccurate results when processing multi‐density urban data. In this article, a data‐driven adaptive hotspot detection (AHD) approach based on kernel density analysis is proposed and applied to various spatial objects. The adaptive search bandwidth is automatically calculated depending on the local density. Window detection is used to extract the specific hotspots in AHD, thus realizing a small‐scale characterization of urban hotspots. Through the trajectory data of Harbin City taxis and New York City crime data, Geo‐information Tupu is used to analyze the obtained specific hotspots and verify the effectiveness of AHD, providing new ideas for further research.

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“…Ripley's function is a popular method for point pattern analysis(Bennett et al, 2023). It is a cumulative functionthat describes the distribution of distances within a point pattern.…”

mentioning

confidence: 99%

A data‐driven adaptive geospatial hotspot detection approach in smart cities

Yan,

Quan,

Wang

2024

Transactions in GIS

View full text Add to dashboard Cite

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Effect of molecular permanent dipole moment on guest aggregation and exciton quenching in phosphorescent organic light emitting diodes

Niyonkuru,

Bennett,

Zachman

et al. 2024

The Journal of Chemical Physics

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This study explores the effect of molecular permanent dipole moment (PDM) on aggregation of guest molecules in phosphorescent host–guest organic light-emitting diodes (OLEDs). Through a combination of photoluminescence measurements, high-angle annular dark-field scanning transmission electron microscopy analysis, and an Ising model based physical vapor-deposition simulation, we show that higher PDM of tris[2-phenylpyridinato-C2,N]iridium(III) guest can actually lead to a reduced aggregation relative to tris[bis[2-(2-pyridinyl-N)phenyl-C] (acetylacetonato)iridium(III) when doped into a non-polar host 1,3,5-tris(carbazol-9-yl)benzene. This study further explores the effect of host polarity by using a polar host 3′,5′-di(carbazol-9-yl)-[1,1′-biphenyl]-3,5-dicarbonitrile, and it is shown that the polar host leads to reduced guest aggregation. This study provides a comprehensive understanding of the impact of molecular PDM on OLED material efficiency and stability, providing insights for optimizing phosphorescent OLED materials.

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Testing Outlier Detection Algorithms for Identifying Early Stage Solute Clusters in Atom Probe Tomography

Stroud,

Al-Saffar,

Carter

et al. 2024

Microscopy and Microanalysis

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Atom probe tomography (APT) is commonly used to study solute clustering and precipitation in materials. However, standard techniques used to identify and characterize clusters within atom probe data, such as the density-based spatial clustering applications with noise (DBSCAN), often underperform with respect to small clusters. This is a limitation of density-based cluster identification algorithms, due to their dependence on the parameter Nmin, an arbitrary lower limit placed on detectable cluster sizes. Therefore, this article attempts to consider the characterization of clustering in atom probe data as an outlier detection problem of which k-nearest neighbors local outlier factor and learnable unified neighborhood-based anomaly ranking algorithms were tested against a simulated dataset and compared to the standard method. The decision score output of the algorithms was then auto thresholded by the Karcher mean to remove human bias. Each of the major models tested outperforms DBSCAN for cluster sizes of <25 atoms but underperforms for sizes >30 atoms using simulated data. However, the new combined k-nearest neighbors (k-NN) and DBSCAN method presented was able to perform well at all cluster sizes. The combined k-NN and seven methods are presented as a new approach to identifying clusters in APT.

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Cluster characterization in atom probe tomography: Machine learning using multiple summary functions

Cited by 3 publications

References 30 publications

A data‐driven adaptive geospatial hotspot detection approach in smart cities

A data‐driven adaptive geospatial hotspot detection approach in smart cities

Effect of molecular permanent dipole moment on guest aggregation and exciton quenching in phosphorescent organic light emitting diodes

Testing Outlier Detection Algorithms for Identifying Early Stage Solute Clusters in Atom Probe Tomography

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