Research Opportunity Number: GEO-01
Project Title: Evaluating the Structure and Chemistry of Exoplanets Using Machine Learning
Project Summary: With thousands of confirmed exoplanets, many possessing core-mantle structures similar to Earth’s, the search for Earth-like habitable planets beyond our solar system has never been more promising. Characterizing the interior structures and chemical layering of these exoplanets is a crucial first step in evaluating their potential habitability.
Current models often rely on mass and radius observations to infer interior compositions but are constrained by assumptions of Earth-like compositions and simplified thermal states of materials. This project seeks to address these limitations by integrating machine learning with thermodynamic principles to model a broader range of planetary compositions and temperature-dependent material phases. The enhanced models will be applied to real exoplanet data from resources like the NASA Exoplanet Archive, providing a more comprehensive understanding of the diversity of planetary interiors. This work combines computational innovation with physical science, offering students an opportunity to engage in impactful research at the intersection of data science and planetary science.
Student Roles and Responsibilities: 1) Collecting and evaluating available thermodynamic model for planetary materials 2) Developing machine learning of predicting the structures and chemistry of exoplanets 3) Applying the updated machine learning model to existing exoplanet databases, such as the NASA Exoplanet Archive. Using techniques involving machine learning and thermodynamics.
Additional Considerations: This project will require knowledge of Python, machine learning basics, basic thermodynamics, differential equation and linear algebra.
Department/Institute: Department of Geosciences
Participation Dates: July 15 – August 20, 2025
Stipend Offered: $0
Number of Internships Available: 0-1
Application Deadline: March 15, 2025, midnight Eastern Daylight Time