Developing A Quantum Computing Workflow for the Investigation of Gold Nanoparticles

Classical computing methods such as density functional theory (DFT) and time dependent density functional theory (TDDFT) have revolutionized chemistry, allowing researchers to gain insight into the electronic structure of a wide range of chemical systems. Specifically, DFT and TDDFT have been employed, by our group and others, to probe the unique structural, electronic, electrochemical, and optical properties of atomically precise gold nanocluster systems. In this dissertation, we present a series of computational works on how DFT and TDDFT were successfully applied to analyze the interesting structure-property relationships of ligated gold nanoparticles. These works include analysis of the systems Au32(R3P)12Cl8, Au20(tBu3P)8, Au9(PPh3)8GaCl22+, and Au32Br8[C16TA+•Br-]12. While DFT and TDDFT have been successful in the characterization of gold nanoparticles, our group and others have found that classical computing methods struggle when simulating systems with hundreds to thousands of atoms in a reasonable timeframe. As the number of atoms in a system increases, the capability of classical computers decreases. In the last few decades, quantum computing has been proposed as the answer to the problems faced while utilizing classical computers. Quantum computers, in theory, are not bound by the same limitations as classical systems, however, current hardware restrictions prevent them from simulating complex systems on their own. Several advancements must be made before quantum computers can study systems larger than just a few atoms. Quantum-DFT embedding alleviates these restrictions by combing both classical and quantum computing. Quantum-DFT embedding makes the process of studying larger systems less demanding by dividing these systems into smaller, more manageable subsystems. This dissertation aims at utilizing quantum-DFT embedding to gain insight on the stability and reactivity of gold nanoparticles. This work acts as a proof-of-concept study that quantum-DFT embedding can be applied to study large systems such as metal nanoparticles.

Author

• Pollard, Nia Ashley

• Aluminum Clusters
• Density Functional Theory
• DFT Embedding
• Nanoparticles
• Quantum Computing
• Structure-Property Relationships

Identifier

hdl:1920/14488

OAI identifier oai:identifier

oai:MARS:1920/14488