Computational materials science based on data-driven approach has gained increasing interest in recent years. The capability of trained machine learning (ML) models, such as an artificial neural network (ANN), to predict the material properties without repetitive calculations is an appealing idea to save computational time. Thermal conductivity in single or multilayer structure is a quintessential property that plays a pivotal role in electronic applications. In this work, we exemplified a data-driven approach based on ML and high-throughput computation (HTC) to investigate the cross-plane thermal transport in multilayer stanene. Stanene has attracted considerable attention due to its novel electronic properties such as topological insulating features with a wide bandgap, making it an appealing candidate to ferry current in electronic devices. Classical molecular dynamics simulations are performed to extract the lattice thermal conductivities (κ_L). The calculated cross-plane κ_L is orders of magnitude lower than its lateral counterparts. Impact factors such as layer number, system temperature, interlayer coupling strength, and compressive/tensile strains are explored. It is found that κ_L of multilayer stanene in the cross-plane direction can be diminished by 86.7% with weakened coupling strength, or 66.6% with tensile strains. A total of 2700 κ_L data are generated using HTC, which are fed into 9 different ANN models for training and testing. The best prediction performance is given by the 2-layer ANN with 30 neurons in each layer.

近年来，基于数据驱动方法的计算材料科学引起了越来越多的兴趣。训练有素的机器学习（ML）模型（例如人工神经网络（ANN））无需重复计算即可预测材料特性的能力是一种节省计算时间的诱人想法。单层或多层结构中的导热率是一种典型的特性，在电子应用中起着举足轻重的作用。在这项工作中，我们举例说明了基于ML和高通量计算（HTC）的数据驱动方法，以研究多层锡中的跨平面热传输。Stanene由于其新颖的电子特性（例如具有宽禁带的拓扑绝缘特征）而吸引了相当多的关注，使其成为吸引电子设备中电流的诱人候选人。κ_大号）。所计算出的横切面κ_大号是数量级比其横向同行低。探讨了影响因素，例如层数，系统温度，层间耦合强度和压缩/拉伸应变。据发现，κ_大号在横向平面内方向上多层stanene可以由86.7％与弱化耦合强度，或者与拉伸应变66.6％减少。总共2700个的κ_大号数据使用HTC，其被进料至9个不同的人工神经网络模型进行训练和测试产生的。最好的预测性能由2层ANN提供，每层30个神经元。