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dc.contributor.authorVerpoort, Philipp
dc.date.accessioned2021-04-24T14:14:29Z
dc.date.available2021-04-24T14:14:29Z
dc.date.submitted2021-12-02
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/321550
dc.description.abstractThis thesis reports results of studies conducted on the equilibration of two systems and consists of two parts: the first part deals with the optimisation of deep neural networks, whereas the second part with the decay of non-equilibrium states in strongly Rashba-coupled systems at low temperature. Deep learning is a conceptually simple, highly effective, and widely used tool, yet there remains insufficient understanding for why it works. The optimisation of deep neural networks with common algorithms such as stochastic gradient descent performs unexpectedly well given the complexity of the underlying high-dimensional non-convex minimisation problem. The first part of this thesis therefore looks at the optimisation procedure from the perspective of statistical physics. This allows us to interpret the loss function landscape of deep neural networks as the counterpart of the potential energy landscape in molecular systems and the optimisation of the network as its equilibration dynamics. Using landscape exploration tools developed in theoretical chemistry, we resolve the structure of the loss function landscape, from which we can draw conclusions for the relaxational dynamics of typical optimisers and, consequently, for deep learning. The second part investigates how a non-equilibrium charge-carrier chirality distribution in a clean, strongly Rashba-coupled system at low temperatures decays over time. We first motivate this analysis based on experimental studies of transport properties in Rashba materials at low temperatures and subject to external magnetic fields. We investigate whether chirality imbalances could serve as the source for those experimental observations and develop a framework that models the behaviour of such a system. We then proceed with a more general theoretical study of the equilibration mechanisms of chirality in low-temperature strongly Rashba-coupled systems and compute the relaxation timescales of those mechanisms.
dc.description.sponsorshipThis thesis is the outcome of doctoral studies conducted at the University of Cambridge with the financial support of the Engineering and Physical Sciences Research Council of the UK.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCondensed-matter physics
dc.subjectMachine learning
dc.subjectDeep neural networks
dc.subjectLoss-function landscapes
dc.subjectDeep learning
dc.subjectSolid-state physics
dc.subjectRashba effect
dc.subjectNon-equilibrium physics
dc.titleEquilibration of deep neural networks and carrier chirality in Rashba systems
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.identifier.doi10.17863/CAM.68669
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.contributor.orcidVerpoort, Philipp [0000-0003-1319-5006]
rioxxterms.typeThesis
dc.publisher.collegeTrinity Hall
dc.type.qualificationtitlePhD in Physics
pubs.funder-project-idEPSRC (2138907)
cam.supervisorConduit, Gareth
cam.supervisor.orcidConduit, Gareth [0000-0003-3807-6361]


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Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)