Aaron A
- Research Program Mentor
PhD candidate at Stanford University
Expertise
materials science, light-matter interaction (novel materials and nanostructures), high-performance computing, (quantum) chemistry, solar panels, algorithmic development/stochastic algorithms
Bio
I'm a 4th year PhD student interested in developing materials and technologies that can help generate clean energy more efficiently and help major industries become more sustainable. By using massive supercomputers to perform highly accurate quantum simulations, we are able to understand how certain materials can improve solar panel efficiency or how sunlight can be used to decompose plastics that otherwise stay in the environment for years. We can also design novel qubits and ultra-sensitive sensors for a variety of applications. In my free time I enjoy outdoor activities like running, playing tennis, and backpacking, as well as cooking, doing ceramics, and gardening. I absolutely love mangos and am constantly on the search for them :)Project ideas
Thermal decomposition of water for hydrogen production
Hydrogen is used as a fuel and precursor to many chemical products. One way to create it is to expose water to extreme temperatures, causing the water to decompose into hydrogen and oxygen. In this project we will write a short molecular dynamics code in Python and run a simulation of a few water molecules at high temperature to watch this decomposition occur. Based on our results, we will decide if this approach is economically feasible for large-scale hydrogen production. Outcomes of this project include a working molecular dynamics code and scientific research paper.
Data-driven search for transparent conducting oxides
Transparent conducting oxides (TCOs) such as indium tin oxide (ITO) are a critical component of solar panels, touch-screen devices, and smart windows. However, TCOs are rare, and while ITO is used widely, it is expensive and restricts the possible applications. In this project we will find new TCO candidates by analyzing large databases such as MaterialsProject. A filtering and/or machine learning approach can be employed to parse the database, look for properties that suggest a material will be a TCO and compile a final set of potential candidate materials. Outcomes of this project include a code base to parse the MaterialsProject database and look for good materials, the understanding of basic materials science topics that will be used to come up with good metrics for deciding whether a material might be a TCO, and a research paper.
