Machine learning and artificial neural network prediction of interfacial thermal resistance between graphene and hexagonal boron nitride

Hong Yang, Zhongtao Zhang, Jingchao Zhang, Xiao Cheng Zeng

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

High-performance thermal interface materials (TIMs) have attracted persistent attention for the design and development of miniaturized nanoelectronic devices; however, a large number of potential new materials exist to form these heterostructures and the explorations of their thermal properties are time consuming and expensive. In this work, we train several supervised machine learning (ML) and artificial neural network (ANN) models to predict the interfacial thermal resistance (R) between graphene and hexagonal boron-nitride (hBN) with only the knowledge of the system temperature, coupling strength between two layers, and in-plane tensile strains. The training data were obtained by high-throughput computations (HTCs) of R using classical molecular dynamics (MD) simulations. Four different ML models, i.e., linear regression, polynomial regression, decision tree and random forest, are explored. A pair of one dense layer ANNs and another pair of two dense layer deep neural networks (DNNs) are also investigated. It is reported that the DNN models provide better R prediction results compared to the ML models. The thermal property predictions using HTC and ML/ANN models are applicable to a wide range of materials and open up new perspectives in the explorations of TIMs.

Original languageEnglish (US)
Pages (from-to)19092-19099
Number of pages8
JournalNanoscale
Volume10
Issue number40
DOIs
StatePublished - Oct 28 2018

ASJC Scopus subject areas

  • Materials Science(all)

Fingerprint Dive into the research topics of 'Machine learning and artificial neural network prediction of interfacial thermal resistance between graphene and hexagonal boron nitride'. Together they form a unique fingerprint.

  • Cite this