Correlated electron diffraction and energy-dispersive X-ray for automated microstructure analysis

TY - JOUR

T1 - Correlated electron diffraction and energy-dispersive X-ray for automated microstructure analysis

AU - Duran, E. C.

AU - Kho, Z.

AU - Einsle, J. F.

AU - Azaceta, I.

AU - Cavill, S. A.

AU - Kerrigan, A.

AU - Lazarov, V. K.

AU - Eggeman, A. S.

N1 - Funding Information: This work was supported by the Henry Royce Institute for Advanced Materials, funded through EPSRC grants EP/R00661X/1, EP/S019367/1, EP/P025021/1, EP/S021531/1 and EP/P025498/1. ECD acknowledges financial support from the Republic of Türkiye Ministry of National Education. ASE acknowledges financial support from the Royal Society. ZK thanks the EPSRC for the studentship provided to them through the Department of Materials Doctoral Training Account. IA acknowledges funding through EPSRC grant EP/K03278X/1. Publisher Copyright: © 2023 The Authors

PY - 2023/9/1

Y1 - 2023/9/1

N2 - In this study the effect of merging correlated energy dispersive X-ray (EDS) spectra and electron diffraction data on unsupervised machine learning (clustering) is explored. The combination of data allows second phase coherent precipitates to be identified, that could not be determined from either the individual EDS or diffraction data alone. In order to successfully combine these two distinct data types we leveraged a data fusion method where both data sets were normalised and combined using a robust scaler followed by variance equalisation. A machine learning pipeline was implemented which performs dimensional reduction with PCA and followed by fuzzy C-means clustering, as this allows signals from overlapping regions of the microstructure to be partitioned between different clusters. User control of this partition is used to confirm a change in the stoichiometry of the embedded second phase regions.

AB - In this study the effect of merging correlated energy dispersive X-ray (EDS) spectra and electron diffraction data on unsupervised machine learning (clustering) is explored. The combination of data allows second phase coherent precipitates to be identified, that could not be determined from either the individual EDS or diffraction data alone. In order to successfully combine these two distinct data types we leveraged a data fusion method where both data sets were normalised and combined using a robust scaler followed by variance equalisation. A machine learning pipeline was implemented which performs dimensional reduction with PCA and followed by fuzzy C-means clustering, as this allows signals from overlapping regions of the microstructure to be partitioned between different clusters. User control of this partition is used to confirm a change in the stoichiometry of the embedded second phase regions.

KW - 4D-STEM

KW - Correlated STEM

KW - Fuzzy clustering

KW - Heusler alloys

KW - STEM-EDS

KW - Unsupervised machine learning

UR - http://www.scopus.com/inward/record.url?scp=85163528331&partnerID=8YFLogxK

U2 - 10.1016/j.commatsci.2023.112336

DO - 10.1016/j.commatsci.2023.112336

M3 - Article

AN - SCOPUS:85163528331

SN - 0927-0256

VL - 228

JO - Computational Materials Science

JF - Computational Materials Science

M1 - 112336

ER -