Exploration of Novel Neuromorphic Methodologies for Materials Applications

Derek Gobin, Shay Snyder, Guojing Cong, Shruti R. Kulkarni, Catherine Schuman, Maryam Parsa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Many of today's most interesting questions involve understanding and interpreting complex relationships within graph-based structures. For instance, in materials science, predicting material properties often relies on analyzing the intricate network of atomic interactions. Graph neural networks (GNNs) have emerged as a popular approach for these tasks; however, they suffer from limitations such as inefficient hardware utilization and over-smoothing. Recent advancements in neuromorphic computing offer promising solutions to these challenges. In this work, we evaluate two such neuromorphic strategies known as reservoir computing and hyperdimensional computing. We compare the performance of both approaches for bandgap classification and regression using a subset of the Materials Project dataset. Our results indicate recent advances in hyperdimensional computing can be applied effectively to better represent molecular graphs.

Original languageEnglish
Title of host publicationProceedings - 2024 International Conference on Neuromorphic Systems, ICONS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages282-286
Number of pages5
ISBN (Electronic)9798350368659
DOIs
StatePublished - 2024
Event2024 International Conference on Neuromorphic Systems, ICONS 2024 - Arlington, United States
Duration: Jul 30 2024Aug 2 2024

Publication series

NameProceedings - 2024 International Conference on Neuromorphic Systems, ICONS 2024

Conference

Conference2024 International Conference on Neuromorphic Systems, ICONS 2024
Country/TerritoryUnited States
CityArlington
Period07/30/2408/2/24

Keywords

  • graph neural networks
  • hyperdimensional computing
  • liquid state machine
  • materials science
  • neuromorphic
  • reservoir computing
  • spatial semantic pointers

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