High-Throughput Computations of Cross-Plane Thermal Conductivity in Multilayer Stanene
نویسندگان
چکیده
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 appealing idea save computational time. Thermal conductivity single or multilayer structure a quintessential property that plays pivotal role electronic applications. In this work, we exemplified ML and high-throughput computation (HTC) investigate cross-plane thermal transport stanene. Stanene attracted considerable attention due its novel topological insulating features with wide bandgap, making it candidate ferry current devices. Classical molecular dynamics simulations are performed extract lattice conductivities (?L). calculated ?L orders magnitude lower than lateral counterparts. Impact factors layer number, system temperature, interlayer coupling strength, compressive/tensile strains explored. It found stanene direction can be diminished by 86.7% weakened 66.6% tensile strains. A total 2700 data generated using HTC, which fed into 9 different ANN models for training testing. best prediction performance given 2-layer 30 neurons each layer.
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ژورنال
عنوان ژورنال: International Journal of Heat and Mass Transfer
سال: 2021
ISSN: ['1879-2189', '0017-9310']
DOI: https://doi.org/10.1016/j.ijheatmasstransfer.2021.121073