Imperial College London
Portrait Picture

Dr T Ben Britton

Faculty of Engineering, Department of Materials

Visiting Reader
 
 
 
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Contact

 

+44 (0)20 7594 2634b.britton Website

 
 
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Location

 

B301Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{McAuliffe:2020:10.1016/j.ultramic.2020.113132,
author = {McAuliffe, TP and Dye, D and Britton, TB},
doi = {10.1016/j.ultramic.2020.113132},
journal = {Ultramicroscopy},
pages = {1--11},
title = {Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning.},
url = {http://dx.doi.org/10.1016/j.ultramic.2020.113132},
volume = {219},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Electron backscatter diffraction is a widely used technique for nano- to micro-scale analysis of crystal structure and orientation. Backscatter patterns produced by an alloy solid solution matrix and its ordered superlattice exhibit only extremely subtle differences, due to the inelastic scattering that precedes coherent diffraction. We show that unsupervised machine learning (with principal component analysis, non-negative matrix factorisation, and an autoencoder neural network) is well suited to fine feature extraction and superlattice/matrix classification. Remapping cluster average patterns onto the diffraction sphere lets us compare Kikuchi band profiles to dynamical simulations, confirm the superlattice stoichiometry, and facilitate virtual imaging with a spherical solid angle aperture. This pipeline now enables unparalleled mapping of exquisite crystallographic detail from a wide range of materials within the scanning electron microscope.
AU  - McAuliffe,TP
AU  - Dye,D
AU  - Britton,TB
DO  - 10.1016/j.ultramic.2020.113132
EP  - 11
PY  - 2020///
SN  - 0304-3991
SP  - 1
TI  - Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning.
T2  - Ultramicroscopy
UR  - http://dx.doi.org/10.1016/j.ultramic.2020.113132
UR  - https://www.ncbi.nlm.nih.gov/pubmed/33053461
UR  - https://www.sciencedirect.com/science/article/pii/S0304399120302813?via%3Dihub
UR  - http://hdl.handle.net/10044/1/83493
VL  - 219
ER  -
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