- By:
- Mulet, Ian; Lim, Seung-Hwan ; Gautam, Ashish ; Date, Prasanna A; Kulkarni, Shruti R; Cong, Guojing ; Potok, Thomas E; Zhu, Kevin; Parsa, Maryam; Schuman, Catherine
- Page Number:
- 1-7
- Book Title:
- 2026 Neuro Inspired Computational Elements (NICE)
- Publication Date:
- September 11, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- 2026 Neuro Inspired Computational Elements (NICE)
- Conference Location:
- Atlanta, Georgia, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/NICE69539.2026.11567459
Abstract
Predicting properties of inorganic materials is a heavily researched topic, with several new prediction approaches emerging as competitors. One such competitor is graph neural networks, which leverage the structure of the graph to aid in the prediction process. In this work, we propose integration of neuromorphic computation into the graph neural network pipeline. We call this approach Neuromorphic Graph Learning (NGL). We utilize the NGL approach to leverage evolutionary algorithms and a novel Spike Pipeline for Raster Analysis (SPIRE) for the prediction of band gap in inorganic materials.
