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

^*Corresponding author for this work

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
Early online date	16 Jul 2025
DOIs	https://doi.org/10.1039/d5fd00101c
Publication status	Published - 1 Feb 2026

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10.1039/d5fd00101cLicence: CC BY

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 - Meng, Siguang

AU - Aiwanshu, Xu

AU - Lewis, David J.

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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.

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

Abstract

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

Abstract

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High Entropy Sulfides as Corrosion Resistant Electrocatalysts for the Oxygen Evolution Reaction

The Royce: Capitalising on the Investment

Sir Henry Royce Institute - Manchester and NNL Equipment

Cite this