Applied machine learning for atom probe tomography

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorLomakin, Ivan
dc.contributor.authorWu, Hao
dc.contributor.schoolSähkötekniikan korkeakoulufi
dc.contributor.supervisorAlava, Mikko
dc.date.accessioned2022-12-25T18:00:18Z
dc.date.available2022-12-25T18:00:18Z
dc.date.issued2022-12-16
dc.description.abstractAtom Probe Tomography (APT) is a significant method in exploring the crystal structure. Former work in APT focuses on detecting the crystal based on physics principles. This paper raises a new data-based method for the reconstruction of sample crystal structure, implementing machine learning (ML) models with the detector information as input. First, a simulation for field evaporation process in APT is provided. Next, various ML models are built to obtain the 3D reconstructed atoms distribution image. The discussion of ML models' performance is provided.en
dc.format.extent38+9
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/118604
dc.identifier.urnURN:NBN:fi:aalto-202212257341
dc.language.isoenen
dc.locationP1fi
dc.programmeAEE - Master's Programme in Automation and Electrical Engineering (TS2013)fi
dc.programme.majorControl, Robotics and Autonomous Systemsfi
dc.programme.mcodeELEC3025fi
dc.subject.keywordmachine learningen
dc.subject.keywordatom probe tomographyen
dc.subject.keywordBayesian methoden
dc.subject.keywordartificial neural networken
dc.subject.keywordreconstructionen
dc.titleApplied machine learning for atom probe tomographyen
dc.typeG2 Pro gradu, diplomityöfi
dc.type.ontasotMaster's thesisen
dc.type.ontasotDiplomityöfi
local.aalto.electroniconlyyes
local.aalto.openaccessyes

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