Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
Nanoscale. 2018 Oct 18;10(40):19092-19099. doi: 10.1039/c8nr05703f.
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.
高性能热界面材料（TIMs）在小型化纳米电子器件的设计和开发中引起了持续的关注；然而，存在大量潜在的新材料可以形成这些异质结构，并且对其热性能的探索既耗时又昂贵。在这项工作中，我们训练了几个监督机器学习（ML）和人工神经网络（ANN）模型，仅使用系统温度、两层之间的耦合强度和平面拉伸应变的知识来预测石墨烯和六方氮化硼（hBN）之间的界面热阻（R）。训练数据是通过使用经典分子动力学（MD）模拟对 R 进行高通量计算（HTC）获得的。我们探索了四种不同的 ML 模型，即线性回归、多项式回归、决策树和随机森林。还研究了一对密集层神经网络（DNN）和另一对两个密集层深度神经网络（DNN）。据报道，与 ML 模型相比，DNN 模型提供了更好的 R 预测结果。使用 HTC 和 ML/ANN 模型进行热性能预测适用于广泛的材料，为 TIMs 的探索开辟了新的视角。
