Perspective: Predicting and optimizing thermal transport properties with machine learning methods
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 difficult...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-05-01
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| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546822000143 |
| _version_ | 1811235257960103936 |
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| author | Han Wei Hua Bao Xiulin Ruan |
| author_facet | Han Wei Hua Bao Xiulin Ruan |
| author_sort | Han Wei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T11:49:11Z |
| format | Article |
| id | doaj.art-34b30db329174993873bf31a67674919 |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2024-04-12T11:49:11Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-34b30db329174993873bf31a676749192022-12-22T03:34:15ZengElsevierEnergy and AI2666-54682022-05-018100153Perspective: Predicting and optimizing thermal transport properties with machine learning methodsHan Wei0Hua Bao1Xiulin Ruan2University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, ChinaUniversity of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China; Corresponding authors.School of Mechanical Engineering and Birck Nanotechnology Center, Purdue University, West Lafayette, IN 47907, United States; Corresponding authors.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.http://www.sciencedirect.com/science/article/pii/S2666546822000143Thermal transport propertiesMachine learningPredictionOptimization |
| spellingShingle | Han Wei Hua Bao Xiulin Ruan Perspective: Predicting and optimizing thermal transport properties with machine learning methods Energy and AI Thermal transport properties Machine learning Prediction Optimization |
| title | Perspective: Predicting and optimizing thermal transport properties with machine learning methods |
| title_full | Perspective: Predicting and optimizing thermal transport properties with machine learning methods |
| title_fullStr | Perspective: Predicting and optimizing thermal transport properties with machine learning methods |
| title_full_unstemmed | Perspective: Predicting and optimizing thermal transport properties with machine learning methods |
| title_short | Perspective: Predicting and optimizing thermal transport properties with machine learning methods |
| title_sort | perspective predicting and optimizing thermal transport properties with machine learning methods |
| topic | Thermal transport properties Machine learning Prediction Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2666546822000143 |
| work_keys_str_mv | AT hanwei perspectivepredictingandoptimizingthermaltransportpropertieswithmachinelearningmethods AT huabao perspectivepredictingandoptimizingthermaltransportpropertieswithmachinelearningmethods AT xiulinruan perspectivepredictingandoptimizingthermaltransportpropertieswithmachinelearningmethods |