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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

Project ideas are meant to help inspire student thinking about their own project. Students are in the driver seat of their research and are free to use any or none of the ideas shared by their mentors.

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.

Coding skills

Python, Fortran

Languages I know

Russian (beginner)

Teaching experience

I have been a course assistant for a few classes at Stanford University, have tutored both privately and with a volunteer organization for middle schoolers. On the non-academic side I have been a ballroom dance and ceramics teacher for a several years.

Credentials

Work experience

Stanford University Physics Department (2018 - 2018)
Research Intern
Stanford University Applied Physics Department (2018 - 2019)
Undergraduate Researcher
Aalto Univeristy (2019 - 2019)
Research Intern
Stanford University Materials Science Department (2020 - 2021)
Undergraduate Researcher

Education

Stanford University
BS Bachelor of Science (2021)
Mathematics
Stanford University
MS Master of Science (2023)
Applied Physics
Stanford University
PhD Doctor of Philosophy candidate
Materials Science and Engineering

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