Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage
Abstract The high-entropy strategy has emerged as a prevalent approach to boost capacitive energy-storage performance of relaxors for advanced electrical and electronic systems. However, exploring high-performance high-entropy systems poses challenges due to the extensive compositional space. Herein...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56443-3 |
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| author | Xingcheng Wang Ji Zhang Xingshuai Ma Huajie Luo Laijun Liu Hui Liu Jun Chen |
| author_facet | Xingcheng Wang Ji Zhang Xingshuai Ma Huajie Luo Laijun Liu Hui Liu Jun Chen |
| author_sort | Xingcheng Wang |
| collection | DOAJ |
| description | Abstract The high-entropy strategy has emerged as a prevalent approach to boost capacitive energy-storage performance of relaxors for advanced electrical and electronic systems. However, exploring high-performance high-entropy systems poses challenges due to the extensive compositional space. Herein, with the assistance of machine learning screening, we demonstrated a high energy-storage density of 20.7 J cm-3 with a high efficiency of 86% in a high-entropy Pb-free relaxor ceramic. A random forest regression model with key descriptors based on limited reported experimental data were developed to predict and screen the elements and chemical compositions of high-entropy systems. Following basic experiments, a (Bi0.5Na0.5)TiO3-based high-entropy relaxor characterized by fine grains, weakly-coupled and small-sized polar clusters was identified. This resulted in a near-linear polarization behavior and an ultrahigh breakdown strength of 95 kV mm-1. Further, this high-entropy realxor presented a high discharge energy density of 7.7 J cm-3 under discharge rate of about 27 ns, along with superior temperature and fatigue stability. Our results present the data-driven model for efficiently exploring high-performance high-entropy relaxors, demonstrating the potential of machine learning in developing relaxors. |
| format | Article |
| id | doaj-art-7ec7dd73dcd948f6ab20bb5e26dc3571 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-7ec7dd73dcd948f6ab20bb5e26dc35712025-02-02T12:32:26ZengNature PortfolioNature Communications2041-17232025-02-011611810.1038/s41467-025-56443-3Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storageXingcheng Wang0Ji Zhang1Xingshuai Ma2Huajie Luo3Laijun Liu4Hui Liu5Jun Chen6Beijing Advanced Innovation Center for Materials Genome Engineering, Department of Physical Chemistry, University of Science and Technology BeijingSchool of Materials Science and Engineering, Nanjing University of Science and TechnologyBeijing Advanced Innovation Center for Materials Genome Engineering, Department of Physical Chemistry, University of Science and Technology BeijingBeijing Advanced Innovation Center for Materials Genome Engineering, Department of Physical Chemistry, University of Science and Technology BeijingCollege of Materials Science and Engineering, Guilin University of TechnologyBeijing Advanced Innovation Center for Materials Genome Engineering, Department of Physical Chemistry, University of Science and Technology BeijingBeijing Advanced Innovation Center for Materials Genome Engineering, Department of Physical Chemistry, University of Science and Technology BeijingAbstract The high-entropy strategy has emerged as a prevalent approach to boost capacitive energy-storage performance of relaxors for advanced electrical and electronic systems. However, exploring high-performance high-entropy systems poses challenges due to the extensive compositional space. Herein, with the assistance of machine learning screening, we demonstrated a high energy-storage density of 20.7 J cm-3 with a high efficiency of 86% in a high-entropy Pb-free relaxor ceramic. A random forest regression model with key descriptors based on limited reported experimental data were developed to predict and screen the elements and chemical compositions of high-entropy systems. Following basic experiments, a (Bi0.5Na0.5)TiO3-based high-entropy relaxor characterized by fine grains, weakly-coupled and small-sized polar clusters was identified. This resulted in a near-linear polarization behavior and an ultrahigh breakdown strength of 95 kV mm-1. Further, this high-entropy realxor presented a high discharge energy density of 7.7 J cm-3 under discharge rate of about 27 ns, along with superior temperature and fatigue stability. Our results present the data-driven model for efficiently exploring high-performance high-entropy relaxors, demonstrating the potential of machine learning in developing relaxors.https://doi.org/10.1038/s41467-025-56443-3 |
| spellingShingle | Xingcheng Wang Ji Zhang Xingshuai Ma Huajie Luo Laijun Liu Hui Liu Jun Chen Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage Nature Communications |
| title | Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage |
| title_full | Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage |
| title_fullStr | Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage |
| title_full_unstemmed | Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage |
| title_short | Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage |
| title_sort | machine learning assisted composition design of high entropy pb free relaxors with giant energy storage |
| url | https://doi.org/10.1038/s41467-025-56443-3 |
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