Article
Chemistry, Physical
Suiting Ning, Shan Huang, Ziye Zhang, Bin Zhao, Renqi Zhang, Ning Qi, Zhiquan Chen
Summary: The thermoelectric properties of intrinsic n-type β-Ga2O3 were evaluated using first-principles calculations and transport theory. A large Seebeck coefficient and good electron mobility were observed. The lattice thermal conductivity can be reduced by adjusting the grain size. These findings suggest the potential application of β-Ga2O3 in high temperature thermoelectric conversion.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2022)
Article
Materials Science, Multidisciplinary
Lihong Han, Xingrun Chen, Qian Wang, Yingjie Chen, Mingfei Xu, Liyuan Wu, Changcheng Chen, Pengfei Lu, Pengfei Guan
Summary: A machine learning method was used to construct the potential function of Sn materials, showing good agreement with experimental data in predicting thermal conductivity. This approach can be applied to simulate arbitrary chemical composition materials and predict thermal stability.
COMPUTATIONAL MATERIALS SCIENCE
(2021)
Article
Nanoscience & Nanotechnology
Yiwen Song, Praneeth Ranga, Yingying Zhang, Zixuan Feng, Hsien-Lien Huang, Marco D. Santia, Stefan C. Badescu, C. Ulises Gonzalez-Valle, Carlos Perez, Kevin Fern, Robert M. Lavelle, David W. Snyder, Brianna A. Klein, Julia Deitz, Albert G. Baca, Jon-Paul Maria, Bladimir Ramos-Alvarado, Jinwoo Hwang, Hongping Zhao, Xiaojia Wang, Sriram Krishnamoorthy, Brian M. Foley, Sukwon Choi
Summary: This study investigates the thermal conductivity of heteroepitaxial beta-Ga2O3 films, and finds that the thermal conductivity is strongly influenced by film thickness, crystallinity, and substrate offcut angles. Additionally, the thermal conductivity of ((2) over bar 01)-oriented beta-(AlxGal)(2)O-3 thin films grown via MOVPE was characterized, with results showing lower conductivity due to phonon-alloy disorder scattering. These findings provide fundamental insights for the development of beta-Ga2O3 electronic and optoelectronic devices.
ACS APPLIED MATERIALS & INTERFACES
(2021)
Article
Engineering, Electrical & Electronic
Samuel H. Kim, Daniel Shoemaker, Bikramjit Chatterjee, Andrew J. Green, Kelson D. Chabak, Eric R. Heller, Kyle J. Liddy, Gregg H. Jessen, Samuel Graham, Sukwon Choi
Summary: This study investigates the impact of thermal management strategies on the performance of beta-Ga2O3 transistors. The experimental results demonstrate the importance of considering the anisotropic thermal conductivity of the material and the geometrical design of the metal electrodes/interconnects in device layout design to maximize both electrical and thermal performance.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2022)
Article
Physics, Applied
Fernan Saiz, Yenal Karaaslan, Riccardo Rurali, Cem Sevik
Summary: A new interatomic potential parameter set was introduced to predict the thermal conductivity of zirconium trisulfide monolayers in this study. The Tersoff-type force field was parameterized using data collected with first-principles calculations, and non-equilibrium molecular dynamics simulations were utilized for predicting the thermal conductivity. The results showed good agreement in structural, mechanical, and dynamical parameters, with the predicted room temperature lattice thermal conductivity matching well with first-principles values and showing comparable variations with temperature.
JOURNAL OF APPLIED PHYSICS
(2021)
Article
Nanoscience & Nanotechnology
Ymir K. Frodason, Patryk P. Krzyzaniak, Lasse Vines, Joel B. Varley, Chris G. Van de Walle, Klaus Magnus H. Johansen
Summary: The diffusion of the n-type dopant Sn in beta-Ga2O3 was studied using secondary-ion mass spectrometry combined with hybrid functional calculations. It was found that Ga vacancies mediate the migration of Sn through the formation and dissociation of intermittent mobile VGaSnGa complexes. The migration barrier for the VGaSnGa complex was determined to be 3.0 +/- 0.4 eV, consistent with theoretical predictions using the nudged elastic band method.
Article
Chemistry, Physical
Huijuan Wu, Suiting Ning, Xiangbin Chen, Tian Yu, Tingdong Zhang, Xiang Qu, Ning Qi, Zhiquan Chen
Summary: Multiple strategies were employed to suppress the lattice thermal conductivity in beta-Ga2O3, including the introduction of point defects, grain boundaries, secondary phases, and pores. As a result, the as-sintered beta-Ga2O3 exhibited an extremely low thermal conductivity, attributed to the presence of micro-pores, vacancies, and a high porosity.
ACS APPLIED ENERGY MATERIALS
(2022)
Article
Nanoscience & Nanotechnology
Jingjing Shi, Chao Yuan, Hsien-Lien Huang, Jared Johnson, Chris Chae, Shangkun Wang, Riley Hanus, Samuel Kim, Zhe Cheng, Jinwoo Hwang, Samuel Graham
Summary: This study investigates thermal transport at beta-Ga2O3/metal interfaces using theoretical modeling and experimental measurements. It highlights the significant impact of metal cutoff frequency on thermal boundary conductance, followed by chemical reactions and defects. Different metals show varying effects on the thermal boundary conductance in these interfaces.
ACS APPLIED MATERIALS & INTERFACES
(2021)
Article
Engineering, Chemical
Fevzi Sahin, Omer Genc, Murat Gokcek, Andac Batur Colak
Summary: In this study, two different artificial neural networks were created to predict the thermal conductivity and zeta potential of Fe3O4/water nanofluid. The experimental data was used to propose a new mathematical correlation to calculate the thermal conductivity. The results showed that the artificial neural network models can accurately predict the thermal conductivity and zeta potential.
Article
Thermodynamics
Jiaxuan Xu, Han Wei, Hua Bao
Summary: This study applies physics-informed neural networks to investigate heat conduction in porous media and demonstrates accurate predictions for temperature/heat flux fields without any labeled training data, resulting in improved computational efficiency and flexibility. The research findings suggest that physics-informed neural networks are promising tools for studying heat transfer problems in porous media.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2023)
Article
Materials Science, Multidisciplinary
Tao Li, Qing Hou, Jie-chao Cui, Jia-hui Yang, Ben Xu, Min Li, Jun Wang, Bao-qin Fu
Summary: This study investigates the thermal and defect properties of AlN using molecular dynamics simulation, and proposes a new method for selecting interatomic potentials, developing a new model. The developed model demonstrates high computational accuracy, providing an important tool for modeling thermal transport and defect evolution in AlN-based devices.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
So Takamoto, Satoshi Izumi, Ju Li
Summary: A universal interatomic potential is urgently needed in computational materials science to model an arbitrary set of chemical elements. In this study, a new architecture called tensor embedded atom network (TeaNet) is proposed, which incorporates graph convolution neural network (GCN) to represent the iterative propagation of rank >= 2 tensor information. The results show that TeaNet can satisfactorily simulate arbitrary structures and reactions involving the first 18 elements on the periodic table, including C-H molecular structures, metals, amorphous SiO2, and water.
COMPUTATIONAL MATERIALS SCIENCE
(2022)
Article
Materials Science, Multidisciplinary
Mashroor S. Nitol, Doyl E. Dickel, Christopher D. Barrett
Summary: In this study, a machine learned interatomic potential was used to model Zinc, overcoming the limitations of classical interatomic potentials. Validation of the network generated potential showed accurate reproduction of the training database and correct predictions of experimentally observed phenomena. The potential demonstrated good agreement with DFT and experimental calculations, making it a useful tool for simulating Zinc at the molecular dynamics scale.
COMPUTATIONAL MATERIALS SCIENCE
(2021)
Article
Chemistry, Physical
Jonas Busk, Mikkel N. Schmidt, Ole Winther, Tejs Vegge, Peter Bjorn Jorgensen
Summary: This research presents a complete framework for training and recalibrating graph neural network ensemble models to accurately predict energy and forces with calibrated uncertainty estimates. The method is demonstrated and evaluated on challenging datasets, achieving good prediction accuracy and uncertainty calibration.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2023)
Article
Chemistry, Multidisciplinary
Jinsen Han, Qiyu Zeng, Ke Chen, Xiaoxiang Yu, Jiayu Dai
Summary: Investigated the thermal transport properties of InSe monolayer and found discrepancies between theoretical predictions and experimental measurements. Utilized two different methods and emphasized the significant role of four-phonon scattering in InSe monolayer for thermal conductivity.
Review
Chemistry, Physical
Yunsong Pang, Jiajia Zhang, Ruimin Ma, Zhiguo Qu, Eungkyu Lee, Tengfei Luo
ACS ENERGY LETTERS
(2020)
Article
Nanoscience & Nanotechnology
Qiushi Zhang, Robert Douglas Neal, Dezhao Huang, Svetlana Neretina, Eungkyu Lee, Tengfei Luo
ACS APPLIED MATERIALS & INTERFACES
(2020)
Article
Multidisciplinary Sciences
Eungkyu Lee, Dezhao Huang, Tengfei Luo
NATURE COMMUNICATIONS
(2020)
Article
Materials Science, Multidisciplinary
Zhe Cheng, Yee Rui Koh, Abdullah Mamun, Jingjing Shi, Tingyu Bai, Kenny Huynh, Luke Yates, Zeyu Liu, Ruiyang Li, Eungkyu Lee, Michael E. Liao, Yekan Wang, Hsuan Ming Yu, Maki Kushimoto, Tengfei Luo, Mark S. Goorsky, Patrick E. Hopkins, Hiroshi Amano, Asif Khan, Samuel Graham
PHYSICAL REVIEW MATERIALS
(2020)
Article
Nanoscience & Nanotechnology
Yee Rui Koh, Zhe Cheng, Abdullah Mamun, Md Shafkat Bin Hoque, Zeyu Liu, Tingyu Bai, Kamal Hussain, Michael E. Liao, Ruiyang Li, John T. Gaskins, Ashutosh Giri, John Tomko, Jeffrey L. Braun, Mikhail Gaevski, Eungkyu Lee, Luke Yates, Mark S. Goorsky, Tengfei Luo, Asif Khan, Samuel Graham, Patrick E. Hopkins
ACS APPLIED MATERIALS & INTERFACES
(2020)
Article
Physics, Multidisciplinary
Zhe Cheng, Yee Rui Koh, Habib Ahmad, Renjiu Hu, Jingjing Shi, Michael E. Liao, Yekan Wang, Tingyu Bai, Ruiyang Li, Eungkyu Lee, Evan A. Clinton, Christopher M. Matthews, Zachary Engel, Luke Yates, Tengfei Luo, Mark S. Goorsky, W. Alan Doolittle, Zhiting Tian, Patrick E. Hopkins, Samuel Graham
COMMUNICATIONS PHYSICS
(2020)
Article
Energy & Fuels
Chenxuan Xu, Zheng Bo, Shiwen Wu, Zhenhai Wen, Junxiang Chen, Tengfei Luo, Eungkyu Lee, Guoping Xiong, Rose Amal, Andrew T. S. Wee, Jianhua Yan, Kefa Cen, Timothy S. Fisher, Kostya (Ken) Ostrikov
Article
Chemistry, Multidisciplinary
Qiushi Zhang, Ruiyang Li, Eungkyu Lee, Tengfei Luo
Summary: The study reveals that NPs deposited on the transparent substrate by optical forces play a key role in the nucleation of photothermal surface bubbles. The formation of surface bubbles is influenced by the different laser power density thresholds depending on whether the surface is facing or facing away from the light propagation direction.
Article
Materials Science, Multidisciplinary
R. Li, E. Lee, T. Luo
Summary: This study demonstrates the use of physics-informed neural networks to efficiently solve phonon Boltzmann transport equation for multiscale thermal transport problems. By minimizing residuals and including geometric parameters as input, the proposed method shows superiority in efficiency and accuracy compared to existing numerical solvers, offering promising applications in electronic device thermal design.
MATERIALS TODAY PHYSICS
(2021)
Article
Multidisciplinary Sciences
Zhe Cheng, Ruiyang Li, Xingxu Yan, Glenn Jernigan, Jingjing Shi, Michael E. Liao, Nicholas J. Hines, Chaitanya A. Gadre, Juan Carlos Idrobo, Eungkyu Lee, Karl D. Hobart, Mark S. Goorsky, Xiaoqing Pan, Tengfei Luo, Samuel Graham
Summary: Localized interfacial phonon modes have been observed at a high-quality epitaxial Si-Ge interface at around 12 THz, which significantly contribute to the total thermal boundary conductance. Through molecular dynamics simulations and experimental validation, the impact of these interfacial phonon modes on total thermal boundary conductance has been revealed.
NATURE COMMUNICATIONS
(2021)
Article
Chemistry, Physical
Ruiyang Li, Jian-Xun Wang, Eungkyu Lee, Tengfei Luo
Summary: This study introduces a data-free deep learning scheme, physics-informed neural network (PINN), for solving the phonon Boltzmann transport equation (BTE) with arbitrary temperature gradients. Numerical experiments suggest that the proposed PINN can accurately predict phonon transport under arbitrary temperature gradients and shows great promise for thermal design.
NPJ COMPUTATIONAL MATERIALS
(2022)
Article
Materials Science, Multidisciplinary
Andrew Rohskopf, Ruiyang Li, Tengfei Luo, Asegun Henry
Summary: This article introduces a new method and software for simulating the interaction and energy transfer of vibrational modes in large systems. By rewriting the equations of motion and calculating power transfer between modes, it allows for the prediction and simulation of real-time phonon behavior and energy transport.
MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING
(2022)
Article
Physics, Applied
Ruiyang Li, Eungkyu Lee, Tengfei Luo
Summary: In this work, a physics-informed neural network framework is proposed to solve the coupled electron and phonon Boltzmann transport equations. Instead of relying on labeled data, the framework directly learns the spatiotemporal solutions within a parameterized space by enforcing physical laws. The framework demonstrates its efficacy in accurately resolving temperature profiles in low-dimensional thermal transport problems and visualizing ultrafast electron and phonon dynamics in laser heating experiments.
PHYSICAL REVIEW APPLIED
(2023)
Article
Materials Science, Multidisciplinary
R. Li, E. Lee, T. Luo
MATERIALS TODAY PHYSICS
(2020)