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@PHDTHESIS{Saxena:1004505,
author = {Saxena, Alaukik},
othercontributors = {Raabe, Dierk and Berkels, Benjamin and Gault, Baptiste},
title = {{M}achine learning workflows for automatic analysis of atom
probe tomography data},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-01471},
pages = {1 Online-Ressource : Illustrationen},
year = {2024},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2025; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2024},
abstract = {The interaction between various microstructural features
within a material significantly influences its macroscopic
properties. Understanding and measuring the spatial aspects
and composition of these microstructural features is crucial
for developing new materials and investigating how their
structure affects their properties. Atom probe tomography
(APT) has emerged as a key technique for material
characterization due to its high chemical sensitivity and
ability to provide 3D mapping of atomic positions in a
material at sub-nanometer resolution. However, analyzing
these large datasets (often exceeding 10 million atoms) has
traditionally been manual and time-consuming, leading to
potential inconsistencies. The integration of machine
learning, including clustering and deep learning algorithms,
offers a promising solution to streamline this analysis,
making it faster and less reliant on manual efforts. In
response, this thesis introduces several machine
learning-based workflows aimed at automatically quantifying
3D microstructures in APT datasets. These workflows strongly
emphasize robustness and reproducibility, holding the
potential to drive substantial advancements in the field of
materials science. The initial step in analyzing the 3D
microstructure within APT data involves segmenting areas
with similar chemical compositions. These regions are termed
chemical domains and may correspond to various phases
present in a material. The segmentation process is
facilitated by a novel, multi-stage unsupervised machine
learning approach. To gather local composition information,
the APT dataset is divided into uniformly sized voxels. The
coordinate system comprising voxel compositions is called
composition space. To identify distinct phases or chemical
domains within this space, a Gaussian mixture model is
employed for clustering. Additionally, a density-based
clustering algorithm helps isolate different microstructural
features within a single phase at the voxel resolution in
real space, treating each feature as a distinct entity.
These microstructural entities are then examined for their
compositional and geometric properties (such as orientation,
shape, and thickness). The methodology, along with its
limitations and potential enhancements, is illustrated using
both synthetic and actual APT datasets. This includes a
detailed examination of a five-component, Fe-doped Sm-Co
magnetic alloy and a Nickel-based superalloy that
encompasses 30 distinct chemical species. Quantifying 3D
microstructures can be challenging due to their intricate
geometries. To address this, a data analysis pipeline using
supervised machine learning is developed to examine complex
microstructures containing planar subdomains, such as grain
boundaries or intertwined plate-like precipitates. The
pipeline allows for detailed quantification of segmented
planar subdomains, both in terms of their composition (like
in-plane composition fluctuations) and their geometry (such
as in-plane thickness fluctuations). The robustness and
efficiency of this pipeline are exemplified by its
successful application to six different APT datasets
corresponding to the Fe-doped Sm-Co alloy to find the
correlation between material microstructure and magnetic
properties. In order to segment complex linear features,
particularly dislocations, a computational geometry concept
called skeletonization is used to reduce the iso-composition
surface meshes, delineating a microstructure into a linear
graph or skeleton. The skeleton effectively encapsulates the
topological information corresponding to the 3D
iso-composition surface mesh. The underlying skeleton is
used to segment dislocations from the meshes for detailed
composition analysis. On top of this, crystallographic
information from APT data is used to perform orientation
analysis on each dislocation segment to transform it into
the crystal coordinate system. This workflow is able to
successfully extract and analyze dislocations in a Ni-based
alloy and a Fe-Mn alloy, exemplifying its ability to work
seamlessly on different APT datasets and its potential to be
used as a powerful tool to understand the impact of
decorated dislocations on material properties. Although the
3D APT data is spatially noisy, there are still signatures
of the underlying crystal structure of the material in the
data. Smooth overlap of atomic orbitals (SOAP) descriptors,
which are translationally and rotationally invariant, were
used to capture the structural and chemical information
around each atom in the APT data. These descriptions are
optimized using an autoencoder architecture to accommodate
for the noise in the APT data. The optimized descriptors are
used to train a neural network to identify ordered L1$_2$
nano-domains in an Al-Mg-Li alloy. In this thesis, a range
of machine learning-based workflows and models are
presented, which play a crucial role in deciphering the
intricate correlations between material structure and
properties. These models facilitate the extraction of
descriptors related to the structure and composition of
microstructures, laying the ground for training advanced,
high-throughput machine learning models.},
cin = {523110 / 520000},
ddc = {620},
cid = {$I:(DE-82)523110_20140620$ / $I:(DE-82)520000_20140620$},
pnm = {HDS LEE - Helmholtz School for Data Science in Life, Earth
and Energy (HDS LEE) (HDS-LEE-20190612) /
Doktorandenprogramm (PHD-PROGRAM-20170404)},
pid = {G:(DE-Juel1)HDS-LEE-20190612 /
G:(DE-HGF)PHD-PROGRAM-20170404},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-01471},
url = {https://publications.rwth-aachen.de/record/1004505},
}
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