- Predicting Dissolution Rates of Volcanic Glass Using Graph Neural Networks
- Project Year:
2021
- REU Student (s):
Emily Thompson | Southwestern University TX
- Student 1 Institution:
Southwestern University
- Project Mentor:
Shashanka Ubaru
- Project Mentor Area:
IBM Watson Research Center
- Project Abstract:
Graph neural networks (GNNs) in recent years have gained popularity due to the increasing need for machine learning models to accommodate graph structured data. Interdisciplinary fields such as material informatics use graph neural networks to conduct research on materials that are otherwise difficult to analyse. One of these materials, volcanic glass, is used in storing nuclear waste due to their durability in extreme conditions. This durability, measured by its dissolution rate, is difficult to determine in a traditional lab environment due to the extensive time and resources needed to collect and analyse samples. I seek to implement a GNN model that performs regression in order to predict the dissolution rate of ten different types of volcanic glass. In addition to implementing the GNN, I explore varying methods of optimizing the performance of the model. Results demonstrate that unknown glass materials and their accompanying dissolution rate can accurately be determined the GNN in a short period of time. The long term goal for this research is to be able to discover new glass materials based on their dissolution rates.
