Thesis projects:
Nanodesign
Title: Chirality at the nanoscale (MSc and BSc level)
Supervisor: Prof. Francesca Baletto
Description: We would explore the intrinsic chirality of helicenes. Helicenes are polycyclic aromatic compounds in which the benzene rings are connected in ortho positions leading to screw-shaped molecules. They exhibit helical chirality even though they do not contain any asymmetric carbon. We will also consider the effect of adsorbing helicen on a metallic nanoparticle, which shows or not an intrinsic effect.
Project within the EU-CHIRALFORCE network.
Title: Electronic transport in metallic nanoafilaments (MSc)
Supervisor: Prof. Francesca Baletto
Description: To overcome the intrinsic limitations of conventional CPUs based on the von Neumann’s architectures, neuromorphic networks are being explored as alternative computational substitutes due to their massive parallelism and sparse information coding. Neuromorphic engineering attempts to reproduce neural and synaptic behaviour in non-organic hardware devices. One way to mimic the complex functionalities of biological neural networks is through the design of nanoscale devices which can show a non-ohmic behaviour. These nanodevices consists of filaments obtained from metallic nanoparticles which can then be electrically programmed to emulate the behaviour of neurons and synapses. This MSc project will establish design rules for the formation and stability of Cu-nanofilaments in contact with Au-electrodes using molecular dynamics tools. The student will be asked to learn an existing code (LoDiS, see https://github.com/kcl-tscm/LoDiS), she/he will make useful modification (pre- and post-processing) to simulate the assembly of different Cu-nanoparticles between Au-electrodes. We will analyse the structural evolution in a perfect vacuum and in a liquid-like environment. If time allows, calculations of the conductance (as the one reported on figure, TRC is the transmission coefficient in a.u.) of those systems will be carried out, using the software SMEAGOL, and compare with the data we are collecting for Au-based nanofilaments. This project is in collaboration with the D’Agosta group (UPV/EHU and ETSF group, San Sebastian, Spain).
Title: Developing Machine learned force fields (MSc)
Supervisor: Prof. Francesca Baletto
Description: As atoms and ions at the nanoscale move fast, we need to capture at the same time their ionic motion with the best possible accuracy. Machine learning force fields enables us to have the best accuracy at a reasonable computational cost. While several tools have been developed both follwoign Gaussian regression and neural networks, a lot should be to show the real potential of these numerical tools.
In collaboration with Harvard University, we will explore how to use Flare (enad the MLFF) to develop interactomic potential to model AuCu and AuCu in contact with a rich environment, e.g. hydrogen.
Title: Modelling the assembly of metallic nanoparticles (MSc and BSc level)
Supervisor: Prof. Francesca Baletto
Description: Chemical reactions and transformation lie at the heart of advanced materials development and are key to improving the environment through greenhouse gas reduction and pollutant decontamination. Plasmocatalysis is a new and exciting opportunity. As a subfield of photonics, it can provide new solutions to control and manipulate chemical processes with light and partly replace high-cost materials conventionally used to catalyse chemical reactions. Light interacts with the surface of nanoparticles and couples to surface electromagnetic excitations and surface plasmons. The reduced size of the metallic nanoparticles can cause field confinement and ‘localised’ hot-carrier generation, that can be exploit to accelerate chemical reactions, i.e. water splitting. Plasmo(nano)catalysts are generally hetero-structures obtained from the assembly of a large plasmonic nanoparticle, i.e. of gold, decorated with small catalytic nanoparticles, i.e. platinum. This MSc project will study chemical and structural stability of assemble Au-based hetero-structures, using molecular dynamics tools.
The student will be asked to learn an existing code (LoDiS, see https://github.com/kcl-tscm/LoDiS), she/he will make useful modification (pre- and post-processing) to simulate the assembly of small metallic nanoparticles, i.e. Pd, Rh, onto a large Au-core. She/He will consider the use of AI-enabled techniques to explore the structural space of hetero-nanoparticle composition, shape and size to identify optimal systems for specific reactions, using available structural-relationship properties. If time allows, adsorption map of will be created using density functional tools (i.e. Quantum Espresso). The student can contribute to create a database of quantum forces for those systems which will be used in the future to generate new and accurate machine learned force field. The project is part of a UK network CPLAS (https://www.cplas.org/)