Data for "Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine-learning molecular dynamics simulations"

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This is the data set for the preprint arXiv:2206.07605 [cond-mat.mtrl-sci], obtained by the GPUMD code. Here are 6 directories.     1). NEMD     2). NEPpotential     3). PDOS     4). kappa-quenchRate     5). kappa-size     6). kappa-temperature      1). NEMD directory contains calculations of ballistic conductance using NEMD method, where 6 independent cycles are run to average. 2). NEPpotential directory is the trained NEP potential. 3). PDOS directory contains phonon density of states of a-Si samples generated by the quench rate of 10^{11} K/s. 4). kappa-quenchRate directory contains HNEMD calculations of a-Si samples which are prepared using melt-quench temperature protocols with the quench rates covering from 10^{11} to 5x10^{12} K/s. In each case, 3 independent cycles are run. 5). kappa-size directory contains HNEMD calculations based on different supercells. 6 independent cycles are run. 6). kappa-temperature directory contains HNEMD calculations of a-Si samples which are prepared for different targeted temperatures using slow quench rate of 10^{11} K/s.
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2022

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Department of Applied Physics

Miguel A. Caro - Upphovsperson

Tapio Ala-Nissila Orcid -palvelun logo - Upphovsperson

Yanzhou Wang - Upphovsperson

Zheyong Fan - Upphovsperson

Bohai University - Medarbetare

Loughborough University - Medarbetare

University of Science and Technology Beijing - Medarbetare

Zenodo - Utgivare

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Creative Commons Attribution 4.0 International (CC BY 4.0)

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