Development of artificial neural network potential for graphene

Akash Singh, Xin Chen, Yumeng Li, Seid Koric, Erman Guleryuz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Graphene exhibits a unique combination of mechanical, thermal and electrical properties due to the strong and anisotropic bonding, enabling a wide range of novel thermal management and electronic applications. However, it is extremely challenging and costly to investigate graphene solely depending on experimental tests. Atomistic simulation plays an essential role in material system analysis and design and is specifically powerful in characterizing low dimensional materials. However, successful applications of atomistic simulation highly depend on the fidelity and availability of force field potentials for describing the interatomic interactions. Significant discrepancies exist between the simplified empirical potentials and the reference data, and among the empirical potentials themselves. To address the challenge, a new artificial neural network potential is developed for graphene to enable the characterization of the interested properties using molecular dynamics simulations, which is expected to accelerate the discovery and design of novel graphene enabled functional materials.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2020 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105951
DOIs
StatePublished - 2020
EventAIAA Scitech Forum, 2020 - Orlando, United States
Duration: Jan 6 2020Jan 10 2020

Publication series

NameAIAA Scitech 2020 Forum
Volume1 PartF

Conference

ConferenceAIAA Scitech Forum, 2020
CountryUnited States
CityOrlando
Period1/6/201/10/20

ASJC Scopus subject areas

  • Aerospace Engineering

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