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Machine Learning and Artificial Neural Network Prediction of Interfacial Thermal Resistance between Graphene and Hexagonal Boron Nitride

Abstract

High-performance thermal interface materials (TIMs) have attracted persistent attentions 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 system temperature, coupling strength and 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 layers 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.

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Publication details

The article was received on 15 Jul 2018, accepted on 10 Sep 2018 and first published on 11 Sep 2018


Article type: Paper
DOI: 10.1039/C8NR05703F
Citation: Nanoscale, 2018, Accepted Manuscript
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    Machine Learning and Artificial Neural Network Prediction of Interfacial Thermal Resistance between Graphene and Hexagonal Boron Nitride

    H. Yang, Z. Zhang, J. Zhang and X. C. Zeng, Nanoscale, 2018, Accepted Manuscript , DOI: 10.1039/C8NR05703F

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