Predicting the structure of atomically precise semiconductor nanoclusters

Project ID: 2228cd1374 (You will need this ID for your application)

Research Theme: Energy and Decarbonisation

UCL Lead department: Chemistry

Project Summary:

Atomically precise semiconductor nanoclusters (APSNCs) are small structurally well-defined metal oxide, chalcogenide or halide nanoparticles, often capped with coordinating organic ligands, such as phosphines, and/or with surface ions replaced by organic chalcogenide anions, for example phenylthiolate, which can be crystallised out of solution in the form of essentially molecular crystals. In some cases, the inorganic core of these APSNCs have compositions close to known bulk phases, while in other cases they can have compositions not realised in the bulk. A significant number of APSNCs have been reported, differing in composition/stoichiometry and/or size of the inorganic core or ligands on the surface. However, it’s likely that a much larger number have not been discovered because of the size and complexity of the underlying chemical space. Computational structure predicting would be an attractive proposition to complement experimental exploration of the chemical space of potential APSNCs. However, the same complexity is also a problem here. The particles quickly become too big for computational structural exploration with density functional theory to be tractable, while the chemical complexity of ASPNCs means that fitting interatomic potentials, often used to accelerate structure prediction, is extremely difficult, and anyway even if successful would have to be redone for each different composition. To circumvent these issues, we will in this PhD project explore the use of recently developed tight-binding DFT (TB-DFT) methods, which are orders of magnitude faster than DFT and have been parameterised for the whole periodic table. The student will focus on writing a wrapper around the TB-DFT code to make global optimisation using these TB-DFT methods possible, as well as look into grand canonical global optimisation methods to find not just the most stable structure for a given composition/ particle size, but also the most stable structures in a family of compositions and possible particle sizes.