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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
Title | Holographic current density theorem, neural network bootstrapping algorithm and PPM-GW corrected TDDFT(B)-NEGF method in first-principle calculations |
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Authors | |
Advisors | Advisor(s):Chen, G |
Issue Date | 2016 |
Publisher | The 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. |
Abstract | Density 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. |
Degree | Doctor of Philosophy |
Subject | Density functionals Neural networks (Computer science) Green's functions |
Dept/Program | Chemistry |
Persistent Identifier | http://hdl.handle.net/10722/244337 |
DC Field | Value | Language |
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dc.contributor.advisor | Chen, G | - |
dc.contributor.author | Wu, Jiang | - |
dc.contributor.author | 吳江 | - |
dc.date.accessioned | 2017-09-14T04:42:21Z | - |
dc.date.available | 2017-09-14T04:42:21Z | - |
dc.date.issued | 2016 | - |
dc.identifier.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. | - |
dc.identifier.uri | http://hdl.handle.net/10722/244337 | - |
dc.description.abstract | Density 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.language | eng | - |
dc.publisher | The University of Hong Kong (Pokfulam, Hong Kong) | - |
dc.relation.ispartof | HKU Theses Online (HKUTO) | - |
dc.rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works. | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject.lcsh | Density functionals | - |
dc.subject.lcsh | Neural networks (Computer science) | - |
dc.subject.lcsh | Green's functions | - |
dc.title | Holographic current density theorem, neural network bootstrapping algorithm and PPM-GW corrected TDDFT(B)-NEGF method in first-principle calculations | - |
dc.type | PG_Thesis | - |
dc.description.thesisname | Doctor of Philosophy | - |
dc.description.thesislevel | Doctoral | - |
dc.description.thesisdiscipline | Chemistry | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.5353/th_991043953698403414 | - |
dc.date.hkucongregation | 2017 | - |
dc.identifier.mmsid | 991043953698403414 | - |