- Title Information
- Title
- Analyzing Carbon Capture Abilities of MOFs Using Graph Neural Networks
- Abstract
- Excess greenhouse gasses are one of the largest contributors to climate change, a crisis which has had a devastating effect on human beings, biodiversity, and the Earth more broadly. The amount of carbon dioxide specifically, which has been emitted in incredibly large quantities since the Industrial Revolution, has served as a catalyst for the Earth’s warming; yet, emissions are not decreasing quickly enough to prevent temperatures from crossing irreversible thresholds of damage. A potential solution for this problem is Carbon Capture and Storage (CCS); carbon capture is a method for removing or reducing the amount of greenhouse gasses in the atmosphere via some chemical process. One such process is through the use of crystal materials called Metal Organic Frameworks (MOFs). MOFs have a cage-like structure which can capture carbon by trapping carbon dioxide molecules while letting other atmospheric gasses simply pass through the MOF. The challenge is to find the MOF(s) which are best able to capture carbon, whether that be by having the largest capacity for the amount of CO2 molecules it can hold or being particularly selective in trapping CO2 compared to other atmospheric molecules. This, then, leads to the application of machine learning, particularly methods which easily depict chemical structures such as Graph Neural Networks (GNNs), to find the most optimal MOF for capturing carbon. This project uses GNNs to analyze the ability of MOFs to capture carbon holistically, i.e. in which MOFs are able to perform carbon capture such that various metrics of CCS are optimized instead of just one dimension (the literature norm). This project goes on to describe future steps and implications of this work.
- Name:
Personal
- Name Part
- Lapre, Anna L.
- Role
- Role Term (marcrelator)
(authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/cre")
- creator
- Name:
Personal
- Name Part
- Bergen, Karianne
- Role
- Role Term (marcrelator)
(authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/ths")
- thesis advisor
- Name:
Personal
- Name Part
- Freilich, Mara
- Role
- Role Term
- reader
- Name:
Corporate
- Name Part
- Brown University. Computer Science
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2025
- Type of Resource (primo)
- text_resources
- Physical Description
- digitalOrigin
- born digital
- Language
- Language Term:
Text (ISO639-2B)
(authorityURI="http://id.loc.gov/vocabulary/iso639-2.html", valueURI="http://id.loc.gov/vocabulary/iso639-2/eng")
- English
- Note:
thesis
- Senior thesis (ScB)--Brown University, 2025
- Note
(displayLabel="Concentration")
- Applied Mathematics - Computer Science
- Genre (aat)
- theses
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01749583")
- Topic
- Climate change mitigation
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/02032663")
- Topic
- Deep learning (Machine learning)
- Access Condition:
use and reproduction
(href="https://creativecommons.org/licenses/by/4.0/legalcode")
- Attribution 4.0 International (CC BY 4.0)
- Access Condition:
logo
(href="https://licensebuttons.net/l/by/4.0/88x31.png")
- Access Condition:
rights statement
(href="http://rightsstatements.org/vocab/InC/1.0/")
- In Copyright
- Identifier:
DOI
- 10.26300/rhg4-sj26