Thermal boundary resistance at graphene-pentacene interface explored by a data-intensive approach

As the machinery of artificial intelligence matures in recent years, there has been a surge in applying machine learning (ML) techniques for material property predictions. Artificial neural network (ANN) is a branch of ML and has gained increasing popularity due to its capabilities of modeling complex correlations among large datasets. The interfacial thermal transport plays a significant role in the thermal management of graphene-pentacene based organic electronics. In this work, the thermal boundary resistance (TBR) between graphene and pentacene is comprehensively investigated by classical molecular dynamics simulations combined with the ML technique. The TBR values along the a, b and c directions of pentacene at 300 K are 5.19 0.18 x 10(-8) m(2) K W-1, 3.66 0.36 x 10(-8) m(2) K W-1 and 5.03 0.14 x 10(-8) m(2) K W-1, respectively. Different architectures of ANN models are trained to predict the TBR between graphene and pentacene. Two important hyperparameters, i.e. network layer and the number of neurons are explored to achieve the best prediction results. It is reported that the two-layer ANN with 40 neurons each layer provides the optimal model performance with a normalized mean square error loss of 7.04 x 10(-4). Our results provide reasonable guidelines for the thermal design and development of graphene-pentacene electronic devices.

Automated summary

With the maturing of artificial intelligence, machine learning techniques are increasingly applied in predicting material properties. This study explores the thermal boundary resistance between graphene and pentacene using machine learning, providing guidelines for optimizing model performance.