東華大學圖書館 |

Thermal Transport in Solid State: From First-Principles Simulation to Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Thermal Transport in Solid State: From First-Principles Simulation to Machine Learning./
作者:	Liu, Zeyu.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	151 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
Contained By:	Dissertations Abstracts International83-01B.
標題:	Mechanical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28415080
ISBN:	9798516977725

Thermal Transport in Solid State: From First-Principles Simulation to Machine Learning.
Liu, Zeyu.

Thermal Transport in Solid State: From First-Principles Simulation to Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 151 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: B.

Thesis (Ph.D.)--University of Notre Dame, 2021.

This item must not be sold to any third party vendors.

Solid state materials including semiconductors and metals deeply shaped our modern world, from smartphones and light-emitting diodes to electric cars. Besides the electrical transport properties, thermal transport properties also play significant roles in various applications, such as thermoelectrics and electronics thermal management, where thermal transport properties can affect both performance and reliability of the devices. In most semiconductors and insulators, it is the quasiparticle phonon that dominates the thermal transport, which is the quanta of vibrational modes in crystals. A fundamental study of phonon in semiconducting materials is thus of great importance in order to understand how heat is transported and how to better design new nanoscale devices and materials. Electron, on the other hand, contributes significantly to the thermal transport of metals in most cases, while it usually contributes little to thermal transport in semiconductors. It can, however, play a central role in thermoelectrics, where its transport is limited by the electron-phonon scattering. In this way, a comprehensive understanding of coupled electron and phonon properties are needed to fully understand thermal transport in metals and semiconductors.Nowadays, data-driven techniques have emerged as powerful tools for materials design when analytical principles are not established between material compositions and properties. It is thus of great interest and potential to apply the data-driven machine learning techniques to the area of thermal transport in solid state materials. With more data available on thermal transport properties, using machine learning techniques can help us understand and utilize the relations between thermal transport properties and materials structures without complex analytical expressions.In the first part of the thesis, considering the phonon-phonon scattering, lattice thermal conductivities of various two-dimensional (2D) semiconductors are studied by solving the phonon Boltzmann transport equation iteratively with the help of the parameter-free first-principles calculations. We found that a bond saturation rule can work as a general strategy for the design of high lattice thermal conductivity materials. Several ultrahigh thermal conductivity materials, such as the 2D penta-CN2, three-dimensional T12-carbon and AA T12-carbon are predicted.The electron-phonon scattering is then focused in the second part, where thermoelectric properties in semiconductors and the thermal and electrical transport properties of metals are studied in a first-principles calculation scheme. The 2D blue phosphorene is found to be with a low thermoelectric figure of merit, ZT, due to its high lattice thermal conductivity. However, with nanostructures which reduce lattice thermal conductivity, the ZT can be improved dramatically to around 1. In metallic hexagonal NbN, a promising and important superconductor, the first-principles calculation predicted thermal conductivity agrees well with experimental measurements.Finally, machine learning techniques, especially the transfer learning (TL), are applied to study the phonon properties of semiconductors. Considering the fact that electron properties are much easier to obtain. TL is used to leverage electron properties to help predict phonon properties. We found that TL can help improve the prediction accuracy significantly compared with direct training, indicating a deep connection between electron and phonon. Results also indicate that TL can leverage not-so-accurate proxy properties, as long as they encode composition-property relation, to improve models for target properties - a significant feature to materials informatics in general.In general, with the help of both the first-principles calculations and data-driven machine learning techniques, the thermal transport properties of semiconductors and metals are studied. These studies provide us a clearer understanding about how those quasi-particles can interact in solid state materials and they can potentially enable us to design materials and devices with desired thermal transport properties.

ISBN: 9798516977725Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Solid states

Thermal Transport in Solid State: From First-Principles Simulation to Machine Learning.
LDR:05354nmm a2200385 4500 001 2344998
005 20220531062331.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798516977725
035 $a (MiAaPQ)AAI28415080
035 $a AAI28415080
040 $a MiAaPQ $c MiAaPQ
100 1 $a Liu, Zeyu. $3 3683847
245 1 0 $a Thermal Transport in Solid State: From First-Principles Simulation to Machine Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 151 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
500 $a Advisor: Luo, Tengfei.
502 $a Thesis (Ph.D.)--University of Notre Dame, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Solid state materials including semiconductors and metals deeply shaped our modern world, from smartphones and light-emitting diodes to electric cars. Besides the electrical transport properties, thermal transport properties also play significant roles in various applications, such as thermoelectrics and electronics thermal management, where thermal transport properties can affect both performance and reliability of the devices. In most semiconductors and insulators, it is the quasiparticle phonon that dominates the thermal transport, which is the quanta of vibrational modes in crystals. A fundamental study of phonon in semiconducting materials is thus of great importance in order to understand how heat is transported and how to better design new nanoscale devices and materials. Electron, on the other hand, contributes significantly to the thermal transport of metals in most cases, while it usually contributes little to thermal transport in semiconductors. It can, however, play a central role in thermoelectrics, where its transport is limited by the electron-phonon scattering. In this way, a comprehensive understanding of coupled electron and phonon properties are needed to fully understand thermal transport in metals and semiconductors.Nowadays, data-driven techniques have emerged as powerful tools for materials design when analytical principles are not established between material compositions and properties. It is thus of great interest and potential to apply the data-driven machine learning techniques to the area of thermal transport in solid state materials. With more data available on thermal transport properties, using machine learning techniques can help us understand and utilize the relations between thermal transport properties and materials structures without complex analytical expressions.In the first part of the thesis, considering the phonon-phonon scattering, lattice thermal conductivities of various two-dimensional (2D) semiconductors are studied by solving the phonon Boltzmann transport equation iteratively with the help of the parameter-free first-principles calculations. We found that a bond saturation rule can work as a general strategy for the design of high lattice thermal conductivity materials. Several ultrahigh thermal conductivity materials, such as the 2D penta-CN2, three-dimensional T12-carbon and AA T12-carbon are predicted.The electron-phonon scattering is then focused in the second part, where thermoelectric properties in semiconductors and the thermal and electrical transport properties of metals are studied in a first-principles calculation scheme. The 2D blue phosphorene is found to be with a low thermoelectric figure of merit, ZT, due to its high lattice thermal conductivity. However, with nanostructures which reduce lattice thermal conductivity, the ZT can be improved dramatically to around 1. In metallic hexagonal NbN, a promising and important superconductor, the first-principles calculation predicted thermal conductivity agrees well with experimental measurements.Finally, machine learning techniques, especially the transfer learning (TL), are applied to study the phonon properties of semiconductors. Considering the fact that electron properties are much easier to obtain. TL is used to leverage electron properties to help predict phonon properties. We found that TL can help improve the prediction accuracy significantly compared with direct training, indicating a deep connection between electron and phonon. Results also indicate that TL can leverage not-so-accurate proxy properties, as long as they encode composition-property relation, to improve models for target properties - a significant feature to materials informatics in general.In general, with the help of both the first-principles calculations and data-driven machine learning techniques, the thermal transport properties of semiconductors and metals are studied. These studies provide us a clearer understanding about how those quasi-particles can interact in solid state materials and they can potentially enable us to design materials and devices with desired thermal transport properties.
590 $a School code: 0165.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Metals. $3 601053
650 4 $a Semiconductors. $3 516162
650 4 $a Optimization techniques. $3 3681622
650 4 $a High temperature. $3 3681917
650 4 $a Equilibrium. $3 668417
650 4 $a Electric rates. $3 3560701
650 4 $a Energy. $3 876794
650 4 $a Graphene. $3 1569149
650 4 $a Heat conductivity. $3 3681489
650 4 $a Linear algebra. $3 2923381
650 4 $a Crystal structure. $3 3561040
650 4 $a Simulation. $3 644748
650 4 $a Electrical currents. $3 3560296
650 4 $a Velocity. $3 3560495
650 4 $a Integrated circuits. $3 584067
650 4 $a Physics. $3 516296
650 4 $a Nanowires. $3 576100
650 4 $a Experiments. $3 525909
650 4 $a Carbon. $3 604057
650 4 $a Conductivity. $3 3681488
650 4 $a Methods. $3 3560391
650 4 $a Decision trees. $3 827433
650 4 $a Hydrogenation. $3 1244863
650 4 $a Acoustics. $3 879105
653 $a Solid states
653 $a Machine learning
653 $a Semiconductors
653 $a Thermoelectrics
653 $a Boltzmann transport equation
690 $a 0548
690 $a 0791
690 $a 0986
690 $a 0605
710 2 $a University of Notre Dame. $b Aerospace and Mechanical Engineering. $3 3169074
773 0 $t Dissertations Abstracts International $g 83-01B.
790 $a 0165
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28415080