On the use of advanced scanning transmission electron microscopy and machine learning for studying multi-component materials

Alexander S. Eggeman; Christian Maddox; Mark A. Buckingham; Zhiquan Kho; Ran Eitan Abutbul; Siguang Meng; Xu Aiwanshu; David J. Lewis

doi:10.1039/d5fd00101c

On the use of advanced scanning transmission electron microscopy and machine learning for studying multi-component materials

Alexander S. Eggeman
, Christian Maddox
, Mark A. Buckingham
, Zhiquan Kho
, Ran Eitan Abutbul
, Siguang Meng
, Xu Aiwanshu
, David J. Lewis

Research output: Contribution to journal › Article › peer-review

Abstract

The nanoscale distribution of elements in two multi-component materials is assessed by unsupervised machine learning methods. These are compared to elemental maps to highlight the potential shortcomings of simplistic compositional analyses. Quantification of the resulting microstructure components provides insight into the evolution of the microstructure and the possible reasons for misinterpretation of the traditional element maps.

Original language	English
Pages (from-to)	268-278
Number of pages	11
Journal	Faraday Discussions
Volume	264
DOIs	https://doi.org/10.1039/d5fd00101c
State	Published - 1 Feb 2026
Externally published	Yes

ASJC Scopus subject areas

Physical and Theoretical Chemistry

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10.1039/d5fd00101c

Cite this

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title = "On the use of advanced scanning transmission electron microscopy and machine learning for studying multi-component materials",

abstract = "The nanoscale distribution of elements in two multi-component materials is assessed by unsupervised machine learning methods. These are compared to elemental maps to highlight the potential shortcomings of simplistic compositional analyses. Quantification of the resulting microstructure components provides insight into the evolution of the microstructure and the possible reasons for misinterpretation of the traditional element maps.",

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AU - Eggeman, Alexander S.

AU - Maddox, Christian

AU - Buckingham, Mark A.

AU - Kho, Zhiquan

AU - Abutbul, Ran Eitan

AU - Meng, Siguang

AU - Aiwanshu, Xu

AU - Lewis, David J.

PY - 2026/2/1

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N2 - The nanoscale distribution of elements in two multi-component materials is assessed by unsupervised machine learning methods. These are compared to elemental maps to highlight the potential shortcomings of simplistic compositional analyses. Quantification of the resulting microstructure components provides insight into the evolution of the microstructure and the possible reasons for misinterpretation of the traditional element maps.

AB - The nanoscale distribution of elements in two multi-component materials is assessed by unsupervised machine learning methods. These are compared to elemental maps to highlight the potential shortcomings of simplistic compositional analyses. Quantification of the resulting microstructure components provides insight into the evolution of the microstructure and the possible reasons for misinterpretation of the traditional element maps.

UR - https://www.scopus.com/pages/publications/105031012012

U2 - 10.1039/d5fd00101c

DO - 10.1039/d5fd00101c

M3 - Article

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AN - SCOPUS:105031012012

SN - 1359-6640

VL - 264

SP - 268

EP - 278

JO - Faraday Discussions

JF - Faraday Discussions

ER -

On the use of advanced scanning transmission electron microscopy and machine learning for studying multi-component materials

Abstract

ASJC Scopus subject areas

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