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

<p>This is the data set for the preprint&nbsp;<a href="https://arxiv.org/abs/2206.07605">arXiv:2206.07605</a>&nbsp;[cond-mat.mtrl-sci], obtained by the GPUMD code.</p> <p>Here are 6 directories.<br> &nbsp;&nbsp; &nbsp;1). NEMD<br> &nbsp;&nbsp; &nbsp;2). NEPpotential<br> &nbsp;&nbsp; &nbsp;3). PDOS<br> &nbsp;&nbsp; &nbsp;4). kappa-quenchRate<br> &nbsp;&nbsp; &nbsp;5). kappa-size<br> &nbsp;&nbsp; &nbsp;6). kappa-temperature<br> &nbsp;&nbsp; &nbsp;<br> 1). NEMD directory contains calculations of ballistic conductance using NEMD method, where 6 independent cycles are run to average.</p> <p>2). NEPpotential directory is the trained NEP potential.</p> <p>3). PDOS directory contains phonon density of states of a-Si samples generated by the quench rate of 10^{11} K/s.</p> <p>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.</p> <p>5). kappa-size directory contains HNEMD calculations based on different supercells. 6 independent cycles are run.</p> <p>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.</p> <p>&nbsp;</p>