| Project Title |
Quantum Materials for Quantum Computation |
| Project Code |
HPC_21_01149 |
| Principal Investigator |
Professor Ortwin Hess |
| Start Date |
2021-01-01 |
| End Date |
2021-12-31 |
| Abstract |
We plan to investigate materials suitable to create multipartite entanglement as well as perform a quantum repetitor experiment. This will require a multiphysics numerical analysis on the control of light and particles on the nanoscale. We aim to demonstrate the steering of entangled light in ultrathin/2D film structures under near-field excitation. |
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| Project Title |
PIP STUDY |
| Project Code |
HPC_21_01148 |
| Principal Investigator |
Claire Gillan |
| Start Date |
2021-01-07 |
| End Date |
2024-12-07 |
| Abstract |
We need to develop tools that can improve the precision with which we allocate treatments in psychiatry. Current psychiatric disease classifications (DSM-5, ICD-10) ensure reliable diagnoses across clinicians, but their diagnostic categories do not allow for individual treatment predictions. This project aims to remedy this by using machine learning to develop an algorithm that can quantify how likely an individual is to respond to a range of Mental Health Treatments. |
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| Project Title |
Wealth Accumulation, Demographics, and Inter-generational Inequalities |
| Project Code |
HPC_21_01147 |
| Principal Investigator |
Assistant Professor Joseph Kopecky |
| Start Date |
2021-01-01 |
| End Date |
2021-06-01 |
| Abstract |
The aging of populations across the globe due to falling births and increasing life expectancies is profoundly changing the structure of the world economy. Because of different productivity profiles, as well as diverging consumption and saving behaviours across a person's lifetime, any change in demographic structure will have a significant impact on production, financial markets, economic policy and the allocation of resources. This paper studies how the age composition of an economy's population affects the return of its citizens' wealth and their ability to accumulate wealth through their lifetime, with a particular emphasis on effects across the income and wealth distribution and implications for inequality. We build an overlapping generations (OLG) framework simulating saving and investing behaviours across individuals of different ages and across the income and wealth distribution, in an economy with stock, bond and housing markets, and aggregate and idiosyncratic uncertainty. The model is simulated across a wide range of conditions and parameters, and calibrated to match the current and expected future state of a number of advanced economies affected by ageing populations. We then empirically estimate the effect of the age composition of an economy's population on the return on its wealth by way of a large panel dataset using a range of demographic variables and a set of macroeconomic controls. Both model and empirical estimation point to an ageing population leading to a decrease in returns across asset classes, although to a different degree. Because of different asset allocations and consumption propensities at different ages and income and wealth levels, both inter- and intra-generational wealth inequalities are seen increasing, with young consumers in ageing economies particularly affected. |
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| Project Title |
Neureka |
| Project Code |
HPC_20_01146 |
| Principal Investigator |
Claire Gillan |
| Start Date |
2020-12-08 |
| End Date |
2026-10-08 |
| Abstract |
The neureka project gathers large amounts of cognitive and clinical data from users of our smartphone application, neureka. We process these data to develop models to understand risk for developing Alzheimer's Disease and mental health conditions. |
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| Project Title |
Voltage-Induced Electronic and Magnetic Phase Transitions in Strongly Correlated Nano-Devices |
| Project Code |
HPC_20_01145 |
| Principal Investigator |
Dr Andrea Droghetti |
| Start Date |
2020-12-01 |
| End Date |
2022-11-30 |
| Abstract |
This project will demonstrate how different electronic and magnetic phases can be tuned through the application of a bias voltage when a material is incorporated into a two-terminal nano-device, especially in case of materials from the strongly correlated oxides family. The researcher Anita Halder guided by Prof. S. Sanvito and Dr. A. Droghetti will develop and use a solid theoretical approach based on the Non-Equilibrium Green’s functions, Density Functional Theory and Dynamical Mean-Field Theory to predict and establish the fundamental physics of voltage-induced magnetic (ferromagnetic-antiferromagnetic) as well as electronic (metal-insulator) transitions. Devices relying on electronic phase transitions behave as multi-state transistors and resistive switches, which are currently the key hardware components to implement neuromorphic computers. The results of this project may therefore lead to possible technological developments, apart from their fundamental character. It will then set up an extended search for new optimal materials for potential applications via machine learning algorithms. |
|
| Project Title |
Voltage-Induced Electronic and Magnetic Phase Transitions in Strongly Correlated Nano-Devices |
| Project Code |
HPC_20_01144 |
| Principal Investigator |
Dr Andrea Droghetti |
| Start Date |
2020-12-01 |
| End Date |
2022-11-30 |
| Abstract |
This project will demonstrate how different electronic and magnetic phases can be tuned through the application of a bias voltage when a material is incorporated into a two-terminal nano-device, especially in case of materials from the strongly correlated oxides family. The researcher Anita Halder guided by Prof. S. Sanvito and Dr. A. Droghetti will develop and use a solid theoretical approach based on the Non-Equilibrium Green’s functions, Density Functional Theory and Dynamical Mean-Field Theory to predict and establish the fundamental physics of voltage-induced magnetic (ferromagnetic-antiferromagnetic) as well as electronic (metal-insulator) transitions. Devices relying on electronic phase transitions behave as multi-state transistors and resistive switches, which are currently the key hardware components to implement neuromorphic computers. The results of this project may therefore lead to possible technological developments, apart from their fundamental character. It will then set up an extended search for new optimal materials for potential applications via machine learning algorithms. |
|
| Project Title |
Proximity-induced spin and valley Hall effects in stacked van der Waals heterostructures |
| Project Code |
HPC_20_01143 |
| Principal Investigator |
Dr Stephen Power |
| Start Date |
2020-11-01 |
| End Date |
2021-10-31 |
| Abstract |
The two-dimensional (2D) materials have fueled novel technologies with an emphasis on more exotic degrees of freedom, flavours, in a material rather than electrons charge. Such flavours involve spin, the magnetic moment of an electron, and, valley, doubly degenerate state in a graphene band dispersion in low energy regime. In this context, the manipulation and detection of flavour currents is of crucial importance. While graphene offers a highly desirable paradigm for flavour currents transport channel, other 2D materials such as transition metal dichalcogenides are better candidates for generation and manipulation of such flavours. This project aims to combine advantages from each system into a single novel heterostructure device for technological purposes.
The system we propose here consists of two layers, graphene which allows long-ranged propagation and detection using standard electronic approaches and a TMD-based lateral heterostructure in order to induce fine-tunable, spatially-varying spin and valley properties. A lateral heterostructure is an effectively monolayer material with at least two covalently joined 2D materials in the growth direction. Our primary goal is to address how local states at these interfaces influence charge, spin and valley characteristics in the graphene layer of our heterostructures. A secondary goal is the creation of 1D networks of conducting charge and flavour states in the graphene layer, due to these interfaces, which can act as circuitry in complex nanoscale devices.
We employ ab initio approach (DFT packages: SIESTA, QUANTUM Espresso), to determine the exact local structure of such interfaces in monolayers. This will account for effects such as the release of stress at the interface and the charge transfer between different domains. A detailed tight-binding parameterization will next be developed to account for such effects in large scale simulations. This task focuses purely on opening electronic channels by manipulating sublattice-related gaps at either side of the interface. |
|
| Project Title |
Proximity-induced spin and valley Hall effects in stacked van der Waals heterostructures |
| Project Code |
HPC_20_01142 |
| Principal Investigator |
Dr Stephen Power |
| Start Date |
2020-11-01 |
| End Date |
2021-10-31 |
| Abstract |
The two-dimensional (2D) materials have fueled novel technologies with an emphasis on more exotic degrees of freedom, flavours, in a material rather than electrons charge. Such flavours involve spin, the magnetic moment of an electron, and, valley, doubly degenerate state in a graphene band dispersion in low energy regime. In this context, the manipulation and detection of flavour currents is of crucial importance. While graphene offers a highly desirable paradigm for flavour currents transport channel, other 2D materials such as transition metal dichalcogenides are better candidates for generation and manipulation of such flavours. This project aims to combine advantages from each system into a single novel heterostructure device for technological purposes.
The system we propose here consists of two layers, graphene which allows long-ranged propagation and detection using standard electronic approaches and a TMD-based lateral heterostructure in order to induce fine-tunable, spatially-varying spin and valley properties. A lateral heterostructure is an effectively monolayer material with at least two covalently joined 2D materials in the growth direction. Our primary goal is to address how local states at these interfaces influence charge, spin and valley characteristics in the graphene layer of our heterostructures. A secondary goal is the creation of 1D networks of conducting charge and flavour states in the graphene layer, due to these interfaces, which can act as circuitry in complex nanoscale devices.
We employ ab initio approach (DFT packages: SIESTA, QUANTUM Espresso), to determine the exact local structure of such interfaces in monolayers. This will account for effects such as the release of stress at the interface and the charge transfer between different domains. A detailed tight-binding parameterization will next be developed to account for such effects in large scale simulations. This task focuses purely on opening electronic channels by manipulating sublattice-related gaps at either side of the interface. |
|
| Project Title |
Spin-Phonon relaxation in molecular spins |
| Project Code |
HPC_20_01141 |
| Principal Investigator |
Prof Stefano Sanvito |
| Start Date |
2020-11-23 |
| End Date |
2022-11-20 |
| Abstract |
Spin relaxation in magnetic molecules is one of the fundamental issues that prevent their use in quantum technology applications. In this project, I will use DFT and post-Hartree Fock simulations in order to provide insights into the spin-phonon relaxation process in molecular materials. DFT simulations will be used to compute the phonons of a crystal of Vanadium coordination compounds and for a decorated graphene flake. Post HF simulations will then be used to compute how structural dynamics affects the magnetic properties of such compounds. Finally, the spin relaxation time will be evaluated according to the Redfield equations and compared with available experimental results. |
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| Project Title |
Testing and benchmarking the potential use of first-principles DFT+U+J |
| Project Code |
HPC_20_01140 |
| Principal Investigator |
Prof David O'Regan |
| Start Date |
2020-11-05 |
| End Date |
2021-11-05 |
| Abstract |
To establish the regime of reliability and computational viability of first-principles (i.e. self-generating- parameter) DFT+U+J for non-magnetic oxides by testing it on selected properties of interest of selected, representative systems, against appropriate benchmarks. |
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