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postgraduate thesis: Holographic current density theorem, neural network bootstrapping algorithm and PPM-GW corrected TDDFT(B)-NEGF method in first-principle calculations

TitleHolographic current density theorem, neural network bootstrapping algorithm and PPM-GW corrected TDDFT(B)-NEGF method in first-principle calculations
Authors
Advisors
Advisor(s):Chen, G
Issue Date2016
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Wu, J. [吳江]. (2016). Holographic current density theorem, neural network bootstrapping algorithm and PPM-GW corrected TDDFT(B)-NEGF method in first-principle calculations. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.
AbstractDensity Functional Theorem (DFT)-based methods play a major role in modern computational chemistry. Their efficiency has been widely acknowledged and well celebrated. However, their accuracy is limited by a series of approximations. In this thesis, the accuracy and applicability of DFT-based methods are improved and augmented from 3 related perspectives. In Chapter 1, the theoretical justification of time-dependent (TD)-DFT in open system is extended into system subject to an arbitrary external magnetic field, opening up more opportunities to future time-dependent current density functional theorem (TDCDFT)-based methods’ developments and applications in open systems. In Chapter 2, DFT calculated heat of formation (HOF) for small molecules are systematically improved in accuracy by a novel method, the Neural-Network Bootstrapping (NNB) method, which is able to give not only a much more accurate HOF prediction, but also a molecule-specific prediction error estimation. In Chapter 3, Time-dependent Density Functional Tight Binding (TDDFTB)-Non-Equilibrium Green’s Function (NEGF) method in molecular device transport calculation are improved by the addition of explicit electron-electron interaction (EEI) effects calculated from the Plasmon-Pole Model (PPM)-GW method, through introduction of another self-energy in addition to the ones from leads’ coupling. Correspondingly, a new set of equation of motions (EOMs) are derived and implemented. All three developments either directly improved (TD)DFT-based methods’ accuracy or extended the range of applications where (TD)DFT-based methods can be applicable and reliable.
DegreeDoctor of Philosophy
SubjectDensity functionals
Neural networks (Computer science)
Green's functions
Dept/ProgramChemistry
Persistent Identifierhttp://hdl.handle.net/10722/244337

 

DC FieldValueLanguage
dc.contributor.advisorChen, G-
dc.contributor.authorWu, Jiang-
dc.contributor.author吳江-
dc.date.accessioned2017-09-14T04:42:21Z-
dc.date.available2017-09-14T04:42:21Z-
dc.date.issued2016-
dc.identifier.citationWu, J. [吳江]. (2016). Holographic current density theorem, neural network bootstrapping algorithm and PPM-GW corrected TDDFT(B)-NEGF method in first-principle calculations. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.-
dc.identifier.urihttp://hdl.handle.net/10722/244337-
dc.description.abstractDensity Functional Theorem (DFT)-based methods play a major role in modern computational chemistry. Their efficiency has been widely acknowledged and well celebrated. However, their accuracy is limited by a series of approximations. In this thesis, the accuracy and applicability of DFT-based methods are improved and augmented from 3 related perspectives. In Chapter 1, the theoretical justification of time-dependent (TD)-DFT in open system is extended into system subject to an arbitrary external magnetic field, opening up more opportunities to future time-dependent current density functional theorem (TDCDFT)-based methods’ developments and applications in open systems. In Chapter 2, DFT calculated heat of formation (HOF) for small molecules are systematically improved in accuracy by a novel method, the Neural-Network Bootstrapping (NNB) method, which is able to give not only a much more accurate HOF prediction, but also a molecule-specific prediction error estimation. In Chapter 3, Time-dependent Density Functional Tight Binding (TDDFTB)-Non-Equilibrium Green’s Function (NEGF) method in molecular device transport calculation are improved by the addition of explicit electron-electron interaction (EEI) effects calculated from the Plasmon-Pole Model (PPM)-GW method, through introduction of another self-energy in addition to the ones from leads’ coupling. Correspondingly, a new set of equation of motions (EOMs) are derived and implemented. All three developments either directly improved (TD)DFT-based methods’ accuracy or extended the range of applications where (TD)DFT-based methods can be applicable and reliable.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subject.lcshDensity functionals-
dc.subject.lcshNeural networks (Computer science)-
dc.subject.lcshGreen's functions-
dc.titleHolographic current density theorem, neural network bootstrapping algorithm and PPM-GW corrected TDDFT(B)-NEGF method in first-principle calculations-
dc.typePG_Thesis-
dc.description.thesisnameDoctor of Philosophy-
dc.description.thesislevelDoctoral-
dc.description.thesisdisciplineChemistry-
dc.description.naturepublished_or_final_version-
dc.date.hkucongregation2017-
dc.identifier.mmsid991043953698403414-

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