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Perspective: Predicting and optimizing thermal transport properties with machine learning methods

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Energy and AI 2022年第2期8卷 148-161页

作者： Han Wei Hua Bao xiulin ruan University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong UniversityShanghai 200240China School of Mechanical Engineering and Birck Nanotechnology Center Purdue UniversityWest LafayetteIN 47907United States

In recent years, (big) data science has emerged as the “fourth paradigm” in physical science research. Data-driven techniques, e.g. machine learning, are advantageous in dealing with problems of high-dimensional features and complex mappings between quantities, which are otherwise of great difficulty or huge cost with other scientific paradigms. In the past five years or so, there has been a rapid growth of machine learning-assisted research on thermal transport. In this perspective, we review the recent progress in the intersection between machine learning and thermal transport, where machine learning methods generally serve as surrogate models for predicting the thermal transport properties, or as tools for designing structures for the desired thermal properties and exploring thermal transport mechanisms. We provide perspectives about the advantages of machine learning methods in comparison to the physics-based methods for studying thermal transport properties. We also discuss how to improve the accuracy of predictive analytics and efficiency of structural optimization, to provide guidance for better utilizing machine learning-based methods to advance thermal transport research. Finally, we identify several outstanding challenges in this active area as well as opportunities for future developments,including developing machine learning methods suitable for small datasets, discovering effective physics-based descriptors, generating dataset from experiments and validating machine learning results with experiments, and making breakthroughs via discovering new physics.

关键词： Thermal transport properties Machine learning Prediction Optimization

Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity

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npj Computational Materials 2023年第1期9卷 1367-1376页

作者： Ziqi Guo Prabudhya Roy Chowdhury Zherui Han Yixuan Sun Dudong Feng Guang Lin xiulin ruan Department of Mechanical Engineering Purdue UniversityWest Lafayette 47907-2088 INUSA The Birck Nanotechnology Center Purdue UniversityWest Lafayette 47907-2088 INUSA Department of Mathematics Purdue UniversityWest Lafayette 47907-2088 INUSA

Lattice thermal conductivity is important for many applications,but experimental measurements or first principles calculations including three-phonon and four-phonon scattering are expensive or even *** learning approaches that can achieve similar accuracy have been a long-standing open *** recent progress,machine learning models using structural information as descriptors fall short of experimental or first principles *** study presents a machine learning approach that predicts phonon scattering rates and thermal conductivity with experimental and first principles *** success of our approach is enabled by mitigating computational challenges associated with the high skewness of phonon scattering rates and their complex contributions to the total thermal *** learning between different orders of phonon scattering can further improve the model *** surrogates offer up to two orders of magnitude acceleration compared to first principles calculations and would enable large-scale thermal transport informatics.

关键词： spite learning principles

Thermoelectric properties of solution-synthesized n-type Bi2Te3 nanocomposites modulated by Se： An experimental and theoretical study

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Nano Research 2016年第1期9卷 117-127页

作者： Haiyu Fang Je-Hyeong Bahk Tianli Feng Zhe Cheng Amr M. S. Mohammed Xinwei Wang xiulin ruan Ali Shakouri Yue Wu School of Chemical Engineering Purdue University West Lafayette IN 47907 USA Birck Nanotechnology Center Purdue University West Lafayette IN 47907 USA School of Mechanical Engineering Purdue University West Lafayette IN 47907 USA Department of Mechanical Engineering Iowa State University Ames IA 50011 USA Department of Chemical and Biological Engineering Iowa State University Ames IA 50011 USA

We report the investigation of the thermoelectric properties of large-scale solution-synthesized Bi2Te3 nanocomposites prepared from nanowires hot- pressed into bulk pellets. A third element, Se, is introduced to tune the carrier concentration of the nanocomposites. Due to the Se doping, the thermoelectric figure of merit （ZT） of the nanocomposites is significantly enhanced due to the increased power factor and reduced thermal conductivity. We also find that thermal transport in our hot-pressed pellets is anisotropic, which results in different thermal conductivities along the in-plane and cross-plane directions. Theoretical calculations for both electronic and thermal transport are carried out to establish fundamental understanding of the material system and provide directions for further ZT optimization with adjustments to carrier concentration and mobility.

关键词： Bi2Te3 Se doping solution synthesis nanostructure thermoelectric

Highly Weak-light Sensitive and Dual-band Switchable Photodetector Based on CuI/Si Unilateral Heterojunction

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无机材料学报 2024年第9期39卷 1063-1069页

作者： YANG Jialin WANG Liangjun ruan Siyuan JIANG xiulin YANG Chang Key Laboratory of Polar Materials and Devices(MOE) Shanghai Center of Brain-inspired Intelligent Materials and DevicesDepartment of ElectronicsEast China Normal UniversityShanghai 200241China Institute of Intelligent Flexible Mechatronics Jiangsu UniversityZhenjiang 212013China Cell R&D Center JA Solar Holdings Co.LtdYangzhou 225000China

In recent years,copper iodide(CuI)is an emerging p-type wide bandgap semiconductor with high intrinsic Hall mobility,high optical absorption and large exciton binding ***,the spectral response and the photoelectric conversion efficiency are limited for CuI-based heterostructure devices,which is related to the difficulty in fabrication of high-quality CuI thin films on other *** this study,a p-CuI/n-Si photodiode has been fabricated through a facile solid-phase iodination *** the CuI thin film is polycrystalline with obvious structural defects,the CuI/Si diode shows a high weak-light sensitivity and a high rectification ratio of 7.6×10^(4),indicating a good defect *** is because of the unilateral heterojunction behavior of the formation of the p^(+)n *** this work,the mechanism of photocurrent of the p^(+)n diode has been studied *** monochromatic lasers with wavelengths of 400,505,635 and 780 nm have been selected for testing the *** zero-bias voltage,the device is a unilateral heterojunction,and only visible light can be absorbed at the Si *** the other hand,when a bias voltage of-3 V is applied,the photodiode is switched to a broader“UV-visible”band response ***,the detection wavelength range can be switched between the“Visible”and“UV-visible”bands by adjusting the bias ***,the obtained CuI/Si diode was very sensitive to weak light illumination.A very high detectivity of 10^(13)-1014 Jones can be achieved with a power density as low as 0.5μW/cm^(2),which is significantly higher than that of other Cu-based *** findings underscore the high application potential of CuI when integrated with the traditional Si industry.

关键词： er iodide heterojunction photodetector

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非晶态合金催化剂的制备方法及应用

石化技术 2018年第10期25卷 10-10页

作者：王秀林阮树堂江西环境工程职业学院江西赣州341401

非晶态合金催化剂是目前研究比较热门的一类催化剂,具有高选择性及高催化活性的特点。本文主要介绍了非晶态合金催化剂的制备方法及其应用,并展望了非晶态合金催化剂的发展前景。

关键词：非晶态合金催化剂制备方法应用