Loughborough University
sm.pdf (1.37 MB)

Supplementary information files for Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations

Download (1.37 MB)
posted on 2023-02-16, 16:02 authored by Wang, Yanzhou, Fan, Zheyong, Qian, Ping, A, Miguel, Tapio Ala-NissilaTapio Ala-Nissila

Supplementary files for article Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon redicted by machine learning molecular dynamics simulations

Amorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials. Theoretical prediction of the thermal conductivity of a-Si in a wide range of temperatures and sample sizes is still a challenge. Herein we present a systematic investigation of the thermal transport properties of a-Si by employing large-scale molecular dynamics (MD) simulations with an accurate and efficient machine learned neuroevolution potential (NEP) trained against abundant reference data calculated at the quantum-mechanical density-functional-theory level. The high efficiency of NEP allows us to study the effects of finite size and quenching rate in the formation of a-Si in great detail. We find that a simulation cell up to 64000 atoms (a cubic cell with a linear size of 11 nm) and a quenching rate down to 1011 K s−1 are required for almost convergent thermal conductivity. Structural properties, including short- and medium-range order as characterized by the pair-correlation function, angular-distribution function, coordination number, ring statistics, and structure factor are studied to demonstrate the accuracy of NEP and to further evaluate the role of quenching rate. Using both the heterogeneous and homogeneous nonequilibrium MD methods and the related spectral decomposition techniques, we calculate the temperature- and thickness-dependent thermal conductivity values of a-Si and show that they agree well with available experimental results from 10 K to room temperature. Our results also highlight the importance of quantum effects in the calculated thermal conductivity and support the quantum-correction method based on the spectral thermal conductivity.  


Towards accurate computational experimentation (COMPEX): machine-learning-driven simulation of nanocarbon synthesis

Academy of Finland

Find out more...

Next-generation interatomic potentials to simulate new cellulose-based materials (NEXTCELL)

Academy of Finland

Find out more...

Academy of Finland 312298/QTF Center of Excellence program

Multi-scale simulation of flexible thermoelectric materials based on graphene and other two-dimensional materials

National Natural Science Foundation of China

Find out more...

National Key Research and Development Pro-gram of China under Grant No. 2021YFB3802100

China Scholarship Council under Grant No. CSC202006460064



  • Science


  • Mathematical Sciences

Usage metrics

    Mathematical Sciences


    Ref. manager