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Interfacial Thermal Transport and Energy Dissipation in Multilayer PdSe2 Field Effect Transistors

Advanced Materials 2025年第0期 e2503264页

作者： Xie, Jiaqiu Yu, Zehao Sun, Yuanchen Gan, Qikang Liu, Chenhan Wang, Lei Zhang, Lifa Zhao, Yunshan Phonon Engineering Research Center of Jiangsu Province Ministry of Education Key Laboratory of NSLSCS Center for Quantum Transport and Thermal Energy Science Institute of Physics Frontiers and Interdisciplinary Sciences School of Physics and Technology Nanjing Normal University Nanjing210023 China National Laboratory of Solid-State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Micro- and Nano-scale Thermal Measurement and Thermal Management Laboratory Jiangsu Key Laboratory for Numerical Simulation of Large-Scale Complex Systems School of Energy and Mechanical Engineering Nanjing Normal University Nanjing210046 China

The continuous miniaturization of 2D electronic circuits results in increased power density during device operation, leading to heat localization and placing higher demands on their performance thresholds. The risk to thermal breakdown and subsequent damage, due to the energy dissipation in the 2D semiconductor field-effect transistors (FETs) supported on the bulk substrates, represents a significant challenge in maintaining their optimal performance. Herein, this study investigates energy dissipation behavior in multilayer PdSe2 FETs for the first time. The high-field breakdown behavior is firstly studied in multilayer PdSe2 FETs on SiO2/Si substrates, where a maximum current density of ≈2.74 MA cm−2 is observed, which is comparable to that of multilayer black phosphorus FET and significantly higher—by about five times—than that of multilayer MoS2 FET. Additionally, the thermal boundary conductance (TBC) of PdSe2/SiO2 interface is measured at room temperature using Raman thermometry. The TBC is found to be ≈12–13 MW m−2 K−1, which is relatively low compared to the other known solid–solid interfaces, indicating that enhancing the performance of PdSe2 FETs can be possible by optimizing the TBC at the PdSe2/SiO2 interface. These findings provide valuable insights for design of high-quality and high-performance PdSe2 electronic and optoelectronic devices. © 2025 Wiley-VCH GmbH.

关键词： Multilayers

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A force-based gradient descent method for ab initio atomic structure relaxation

arXiv

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arXiv 2022年

作者： Hu, Yukuan Gao, Xingyu Zhao, Yafan Liu, Xin Song, Haifeng State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China

Force-based algorithms for ab initio atomic structure relaxation, such as conjugate gradient methods, usually get stuck in the line minimization processes along search directions, where expensive ab initio calculations are triggered frequently to test trial positions before locating the next iterate. We present a force-based gradient descent method, WANBB, that circumvents the deficiency. At each iteration, WANBB enters the line minimization process with a trial stepsize capturing the local curvature of the energy surface. The exit is controlled by an unrestrictive criterion that tends to accept early trials. These two ingredients streamline the line minimization process in WANBB. The numerical simulations on nearly 80 systems with good universality demonstrate the considerable compression of WANBB on the cost for the unaccepted trials compared with conjugate gradient methods. We also observe across the board significant and universal speedups as well as the superior robustness of WANBB over several widely used methods. The latter point is theoretically established. The implementation of WANBB is pretty simple, in that no a priori physical knowledge is required and only two parameters are present without tuning. © 2022, CC BY.

关键词： Conjugate gradient method

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The effect of f-c hybridization on the γ → α phase transition of cerium studied by lanthanum doping

arXiv

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arXiv 2022年

作者： Wang, Yong-Huan Zhang, Yun Liu, Yu Tan, Xiao Ma, Ce Wang, Yue-Chao Zhang, Qiang Yuan, Deng-Peng Jian, Dan Wu, Jian Lai, Chao Wang, Xi-Yang Luo, Xue-Bing Chen, Qiu-Yun Feng, Wei Liu, Qin Hao, Qun-Qing Liu, Yi Tan, Shi-Yong Zhu, Xie-Gang Song, Hai-Feng Lai, Xin-Chun Science and Technology on Surface Physics and Chemistry Laboratory Sichuan Jiangyou621908 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China Software Center for High Performance Numerical Simulation China Academy of Engineering Physics Beijing100088 China

The hybridization between the localized 4f level (f) with conduction (c) states in γ-Ce upon cooling has been previously revealed in single crystalline thin films experimentally and theoretically, whereas its influence on the γ → α phase transition was not explicitly verified, due to the fact that the phase transition happened in the bulk-layer, leaving the surface in the γ phase. Here in our work, we circumvent this issue by investigating the effect of alloying addition of La on Ce, by means of crystal structure, electronic transport and ARPES measurements, together with a phenomenological periodic Anderson model and a modified Anderson impurity model. Our current researches indicate that the weakening of f-c hybridization is the major factor in the suppression of γ → α phase transition by La doping. The consistency of our results with the effects of other rare earth and actinide alloying additions on the γ → α phase transition of Ce is also discussed. Our work demonstrates the importance of the interaction of f and c electrons in understanding the unconventional phase transition in Ce, which is intuitive for further researches on other rare earth and actinide metals and alloys with similar phase transition behaviors. © 2022, CC BY-NC-ND.

关键词： Crystal structure

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An integrated framework for accelerating reactive flow simulation using GPU and machine learning models

arXiv

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arXiv 2023年

作者： Mao, Runze Wang, Yingrui Zhang, Min Li, Han Xu, Jiayang Dong, Xinyu Zhang, Yan Chen, Zhi X. State Key Laboratory of Turbulence and Complex Systems Aeronautics and Astronautics College of Engineering Peking University Beijing100871 China Beijing100080 China Shanghai SenseTime Intelligent Technology Co. LTD Shanghai200233 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100088 China

Recent progress in artificial intelligence (AI) and high-performance computing (HPC) have brought potentially game-changing opportunities in accelerating reactive flow simulations. In this study, we introduce an open-source computational fluid dynamics (CFD) framework that integrates the strengths of machine learning (ML) and graphics processing unit (GPU) to demonstrate their combined capability. Within this framework, all computational operations are solely executed on GPU, including ML-accelerated chemistry integration, fully-implicit solving of PDEs, and computation of thermal and transport properties, thereby eliminating the CPU-GPU memory copy overhead. Optimisations both within the kernel functions and during the kernel launch process are conducted to enhance computational performance. Strategies such as static data reorganisation and dynamic data allocation are adopted to reduce the GPU memory footprint. The computational performance is evaluated in two turbulent flame benchmarks using quasi-DNS and LES modelling, respectively. Remarkably, while maintaining a similar level of accuracy to the conventional CPU/CVODE-based solver, the GPU/ML-accelerated approach shows an overall speedup of over two orders of magnitude for both cases. This result highlights that high-fidelity turbulent combustion simulation with finite-rate chemistry that requires normally hundreds of CPUs can now be performed on portable devices such as laptops with a medium-end GPU. © 2023, CC BY.

关键词： Graphics processing unit

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A New Subspace Iteration Algorithm for Solving Generalized Eigenvalue Problems

arXiv

引用

arXiv 2022年

作者： Wang, Biyi An, Hengbin Xie, Hehu Mo, Zeyao Institute of Applied Physics and Computational Mathematics Beijing100094 China Graduate School of China Academy of Engineering Physics Beijing100088 China Caep Software Center for High Performance Numerical Simulation Beijing100088 China Icmsec Lsec Ncmis Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

It is needed to solve generalized eigenvalue problems (GEP) in many applications, such as the numerical simulation of vibration analysis, quantum mechanics, electronic structure, etc. The subspace iteration is a kind of widely used algorithm to solve eigenvalue problems. To solve the generalized eigenvalue problem, one kind of subspace iteration method, Chebyshev-Davidson algorithm, is proposed recently. In Chebyshev-Davidson algorithm, the Chebyshev polynomial filter technique is incorporated in the subspace iteration [15]. In this paper, based on Chebyshev-Davidson algorithm, a new subspace iteration algorithm is constructed. In the new algorithm, the Chebyshev filter and inexact Rayleigh quotient iteration techniques are combined together to enlarge the subspace in the iteration. numerical results of a vibration analysis problem show that the number of iteration and computing time of the proposed algorithm is much less than that of the Chebyshev-Davidson algorithm and some typical GEP solution algorithms. Furthermore, the new algorithm is more stable and reliable than the Chebyshev-Davidson algorithm in the numerical *** Codes 65F15, 65N25, 65H17 Copyright © 2022, The Authors. All rights reserved.

关键词： Vibration analysis

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The Establishment of Prediction Model for Soil Liquefaction Based on the Seismic Energy Using the Neural Network

Research Square

引用

Research Square 2021年

作者： Zhang, Yan Chu, Wen-Hui Ahmad, Mahmood Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering College of Civil and Transportation Engineering Hohai University Nanjing210098 China Center for Numerical Simulation Software in Engineering and Sciences College of Mechanics and Materials Hohai University Nanjing211100 China Bannu28100 Pakistan

In the development of the prediction model for soil liquefaction, compared to the stress-based method, the energy-based methods proposed and developed in recent years are closer to the essence of soil liquefaction which is about the energy dissipation. Therefore, considering the weak nonlinear relationship found by the previous research, the fuzzy neural network (FNN) and BP neural network (BPNN) were adopted to try to obtain a prediction model which is the most proper to this nonlinear relationship. Firstly, the database including 284 cases obtained from laboratory test was divided into three separate groups denoted as training, validation set and testing sets by the ratio of 5:1:1;then, the FNN model and BPNN model were iterated to determine the model parameter by referring to the variation of fitness value and relative error of validation set;at the same time, the optimization algorithm of genetic algorithm (GA) was adopted to BPNN to find the best coefficients;besides, the parameter of Cc and D50 was respectively excluded from the database to test their influence degree according to the prediction error;finally, 6 prediction results of FNN and genetic algorithm BP neural network (GABP) were compared with the previously proposed models. The results showed that the relationship of capacity energy to the influencing parameters could not be fitted as a fully linear relationship;the FNN model can learn the role of Cc in affecting the capacity energy while the GABP model needs not to take it into account;the FNN and GABP model all fitted a good weakly nonlinear relationship for the capacity energy, and the GABP model is a better prediction model for capacity energy so far. © 2021, CC BY.

关键词： Fuzzy neural networks

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Localized space-tim e method of fundamental solutions for three-dimensional transient diffusion problem

引用

Acta Mechanica Sinica 2020年第5期36卷 1051-1057,I0002页

作者： L.Qiu J.Lin Q.-H.Qin W.Chen International Center for Simulation Software in Engineering and Sciences College of Mechanics and MaterialsHohai UniversityNanjing 210098China College of Engineering and Computer Science Australian National UniversityCanberraACT 2601Australia State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China State Key Laboratory of Acoustic Institute of AcousticsChinese Academy of SciencesBeijing 100190China

A localized space-time method of fundamental solutions(LSTMFS)is extended for solving three-dimensional transient diffusion problems in this *** interval segmentation in temporal direction is developed for the accurate simulation of long-time-dependent diffusion *** the LSTMFS,the whole space-time domain with nodes arranged i divided into a series of overlapping subdomains with a simple *** each subdomain,the conventional method of fundamental solutions is utilized and the coefficients associated with the considered domain can be explicitly *** calculating a combined sparse matrix system,the value at any node inside the space-time domain can be *** experi-ments demonstrate that high accuracy and efficiency can be simultaneously achieved via the LSTMFS,even for the problems defined on a long-time and quite complex computational domain.

关键词： Localized spacetime method of fundamental solutions Meshless method Long-time evolution Transient diffusion

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Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses

引用

npj Computational Materials 2020年第1期6卷 1-9页

作者： Lu, Zhichao Chen, Xin Liu, Xiongjun Lin, Deye Wu, Yuan Zhang, Yibo Wang, Hui Jiang, Suihe Li, Hongxiang Wang, Xianzhen Lu, Zhaoping Beijing Advanced Innovation Center for Materials Genome Engineering State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing100083 China Institute of Applied Physics and Computational Mathematics Beijing100088 China Software Center for High Performance Numerical Simulation Chinese Academy of Engineering Physics Beijing100088 China Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing100083 China

Fe-based metallic glasses (MGs) have been extensively investigated due to their unique properties, especially the outstanding soft-magnetic properties. However, conventional design of soft-magnetic Fe-based MGs is heavily relied on "trial and error" experiments, and thus difficult to balance the saturation flux density (Bs) and thermal stability due to the strong interplay between the glass formation and magnetic interaction. Herein, we report an eXtreme Gradient Boosting (XGBoost) machine-learning (ML) model for developing advanced Fe-based MGs with a decent combination of Bs and thermal stability. While it is an attempt to apply ML for exploring soft-magnetic property and thermal stability, the developed XGBoost model based on the intrinsic elemental properties (i.e., atomic size and electronegativity) can well predict Bs and Tx (the onset crystallization temperature) with an accuracy of 93.0% and 94.3%, respectively. More importantly, we derived the key features that primarily dictate Bs and Tx of Fe-based MGs from the ML model, which enables the revelation of the physical origins underlying the high Bs and thermal stability. As a proof of concept, several Fe-based MGs with high Tx (>800 K) and high Bs (>1.4 T) were successfully developed in terms of the ML model. This work demonstrates that the XGBoost ML approach is interpretable and feasible in the extraction of decisive parameters for properties of Fe-based magnetic MGs, which might allow us to efficiently design high-performance glassy materials. © 2020, The Author(s).

关键词： Thermodynamic stability

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Assessment and Optimization of a Single Flash Geothermal System Recovered by a Trans-Critical Co2 Cycle Using Different Scenarios

SSRN

引用

SSRN 2023年

作者： Lamagna, Mario Aryanfar, Yashar Hoseinzadeh, Siamak Nastasi, Benedetto Garsia, Davide Astiaso Mohtaram, Soheil Sun, Hong Guang State Key Laboratory of Hydrology-Water Recourses and Hydraulic Engineering International Center for Simulation Software in Engineering and Sciences College of Mechanics and Materials Hohai University Jiangsu Nanjing210098 China Universidad Autónoma de Ciudad Juárez Mexico Department of Planning Design and Technology of Architecture Sapienza University of Rome Rome00196 Italy Department of Earth Sciences and Forecasting Research Center Prevention and Control of Geological Risks China Department of Earth Sciences and Forecasting Research Center Prevention and Control of Geological Risks China School of Energy and Power Engineering University of Shanghai for Science and Technology Shanghai200093 China

Globally, the use of renewable energy, particularly geothermal energy, is rising quickly. Geothermal cycles' low efficiency has repeatedly shown how crucial it is to recover heat lost during these cycles. This study suggests a combined power generation cycle replicating using the EES software environment that combines a single flash cycle with a trans-critical carbon dioxide cycle. The findings demonstrate that, in comparison to the BASIC single flash cycle, the design characteristics of the proposed system are greatly improved. The suggested strategy is then enhanced by employing the genetic algorithm, the Nelder-Mead Simplex method, and the direct algorithm. Separator pressure, steam turbine outlet pressure, and carbon dioxide turbine inlet pressure are three assumed variable parameters, and exergy efficiency is the target parameter. In the default operating mode, the system exergy efficiency is 32.46%, increasing to 39.21% using the genetic algorithm and 36.16% using the Nelder-Mead method and 38.82% using the direct algorithm. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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CONVERGENCE OF THE PLANEWAVE APPROXIMATIONS FOR QUANTUM INCOMMENSURATE SYSTEMS

arXiv

引用

arXiv 2022年

作者： Wang, Ting Chen, Huajie Zhou, Aihui Zhou, Yuzhi Massatt, Daniel School of Mathematical Sciences Beijing Normal University Beijing100875 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100094 China Mathematics Department Louisiana State University LA70802 United States

Incommensurate structures come from stacking the single layers of low-dimensional materials on top of one another with misalignment such as a twist in orientation. While these structures are of significant physical interest, they pose many theoretical challenges due to the loss of periodicity. This paper studies the physical observables of continuum Schrödinger operators for incommensurate systems. We characterize the density of states and local density of states in real and reciprocal spaces, and develop novel numerical methods to approximate them. In particular, we (i) justify the thermodynamic limit of the density of states and local density of states via the operator kernel characterization;and (ii) propose efficient numerical schemes based on planewave approximation and trapezoidal rule in reciprocal space. We present both rigorous analysis and numerical simulations to support the reliability and efficiency of our numerical *** Codes 65Z05, 81-08, 47G30 Copyright © 2022, The Authors. All rights reserved.

关键词： numerical methods

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