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Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted 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....
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| Main Authors: | , , , , |
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| Format: | Default Article |
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2023
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| Online Access: | https://hdl.handle.net/2134/22035311.v1 |
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| _version_ | 1818165552595599360 |
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| author | Yanzhou Wang Zheyong Fan Ping Qian Miguel A Caro Tapio Ala-Nissila |
| author_facet | Yanzhou Wang Zheyong Fan Ping Qian Miguel A Caro Tapio Ala-Nissila |
| author_sort | Yanzhou Wang (519660) |
| collection | Figshare |
| description | 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. |
| format | Default Article |
| id | rr-article-22035311 |
| institution | Loughborough University |
| publishDate | 2023 |
| record_format | Figshare |
| spelling | rr-article-220353112023-02-06T00:00:00Z Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations Yanzhou Wang (519660) Zheyong Fan (1287324) Ping Qian (1643260) Miguel A Caro (12505816) Tapio Ala-Nissila (3814327) Mathematical sciences Other mathematical sciences Other mathematical sciences not elsewhere classified Machine learning Molecular dynamics Thermal conductivity <p>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. </p> 2023-02-06T00:00:00Z Text Journal contribution 2134/22035311.v1 https://figshare.com/articles/journal_contribution/Quantum-corrected_thickness-dependent_thermal_conductivity_in_amorphous_silicon_predicted_by_machine_learning_molecular_dynamics_simulations/22035311 CC BY-NC-ND 4.0 |
| spellingShingle | Mathematical sciences Other mathematical sciences Other mathematical sciences not elsewhere classified Machine learning Molecular dynamics Thermal conductivity Yanzhou Wang Zheyong Fan Ping Qian Miguel A Caro Tapio Ala-Nissila Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations |
| title | Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations |
| title_full | Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations |
| title_fullStr | Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations |
| title_full_unstemmed | Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations |
| title_short | Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations |
| title_sort | quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations |
| topic | Mathematical sciences Other mathematical sciences Other mathematical sciences not elsewhere classified Machine learning Molecular dynamics Thermal conductivity |
| url | https://hdl.handle.net/2134/22035311.v1 |