Harnessing quantum power: Revolutionizing materials design through advanced quantum computation
Abstract The design of advanced materials for applications in areas of photovoltaics, energy storage, and structural engineering has made significant strides. However, the rapid proliferation of candidate materials—characterized by structural complexity that complicates the relationships between fea...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-12-01
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| Series: | Materials Genome Engineering Advances |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/mgea.73 |
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| _version_ | 1841543269476990976 |
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| author | Zikang Guo Rui Li Xianfeng He Jiang Guo Shenghong Ju |
| author_facet | Zikang Guo Rui Li Xianfeng He Jiang Guo Shenghong Ju |
| author_sort | Zikang Guo |
| collection | DOAJ |
| description | Abstract The design of advanced materials for applications in areas of photovoltaics, energy storage, and structural engineering has made significant strides. However, the rapid proliferation of candidate materials—characterized by structural complexity that complicates the relationships between features—presents substantial challenges in manufacturing, fabrication, and characterization. This review introduces a comprehensive methodology for materials design using cutting‐edge quantum computing, with a particular focus on quadratic unconstrained binary optimization (QUBO) and quantum machine learning (QML). We introduce the loop framework for QUBO‐empowered materials design, including constructing high‐quality datasets that capture critical material properties, employing tailored computational methods for precise material modeling, developing advanced figures of merit to evaluate performance metrics, and utilizing quantum optimization algorithms to discover optimal materials. In addition, we delve into the core principles of QML and illustrate its transformative potential in accelerating material discovery through a range of quantum simulations and innovative adaptations. The review also highlights advanced active learning strategies that integrate quantum artificial intelligence, offering a more efficient pathway to explore the vast, complex material design space. Finally, we discuss the key challenges and future opportunities for QML in material design, emphasizing their potential to revolutionize the field and facilitate groundbreaking innovations. |
| format | Article |
| id | doaj-art-45d5be1a7bca4051901b5036fbe287b8 |
| institution | Kabale University |
| issn | 2940-9489 2940-9497 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Materials Genome Engineering Advances |
| spelling | doaj-art-45d5be1a7bca4051901b5036fbe287b82025-01-13T15:15:31ZengWiley-VCHMaterials Genome Engineering Advances2940-94892940-94972024-12-0124n/an/a10.1002/mgea.73Harnessing quantum power: Revolutionizing materials design through advanced quantum computationZikang Guo0Rui Li1Xianfeng He2Jiang Guo3Shenghong Ju4China‐UK Low Carbon College Shanghai Jiao Tong University Shanghai ChinaChina‐UK Low Carbon College Shanghai Jiao Tong University Shanghai ChinaChina‐UK Low Carbon College Shanghai Jiao Tong University Shanghai ChinaGraduate School of Frontier Science The University of Tokyo Kashiwa Chiba JapanChina‐UK Low Carbon College Shanghai Jiao Tong University Shanghai ChinaAbstract The design of advanced materials for applications in areas of photovoltaics, energy storage, and structural engineering has made significant strides. However, the rapid proliferation of candidate materials—characterized by structural complexity that complicates the relationships between features—presents substantial challenges in manufacturing, fabrication, and characterization. This review introduces a comprehensive methodology for materials design using cutting‐edge quantum computing, with a particular focus on quadratic unconstrained binary optimization (QUBO) and quantum machine learning (QML). We introduce the loop framework for QUBO‐empowered materials design, including constructing high‐quality datasets that capture critical material properties, employing tailored computational methods for precise material modeling, developing advanced figures of merit to evaluate performance metrics, and utilizing quantum optimization algorithms to discover optimal materials. In addition, we delve into the core principles of QML and illustrate its transformative potential in accelerating material discovery through a range of quantum simulations and innovative adaptations. The review also highlights advanced active learning strategies that integrate quantum artificial intelligence, offering a more efficient pathway to explore the vast, complex material design space. Finally, we discuss the key challenges and future opportunities for QML in material design, emphasizing their potential to revolutionize the field and facilitate groundbreaking innovations.https://doi.org/10.1002/mgea.73active learning frameworkmaterials design and optimizationquadratic unconstrained binary optimizationquantum machine learning |
| spellingShingle | Zikang Guo Rui Li Xianfeng He Jiang Guo Shenghong Ju Harnessing quantum power: Revolutionizing materials design through advanced quantum computation Materials Genome Engineering Advances active learning framework materials design and optimization quadratic unconstrained binary optimization quantum machine learning |
| title | Harnessing quantum power: Revolutionizing materials design through advanced quantum computation |
| title_full | Harnessing quantum power: Revolutionizing materials design through advanced quantum computation |
| title_fullStr | Harnessing quantum power: Revolutionizing materials design through advanced quantum computation |
| title_full_unstemmed | Harnessing quantum power: Revolutionizing materials design through advanced quantum computation |
| title_short | Harnessing quantum power: Revolutionizing materials design through advanced quantum computation |
| title_sort | harnessing quantum power revolutionizing materials design through advanced quantum computation |
| topic | active learning framework materials design and optimization quadratic unconstrained binary optimization quantum machine learning |
| url | https://doi.org/10.1002/mgea.73 |
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