Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm
Abstract Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. Here, we present a framework named Material G...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55613-z |
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| author | Zhilong Song Linfeng Fan Shuaihua Lu Chongyi Ling Qionghua Zhou Jinlan Wang |
| author_facet | Zhilong Song Linfeng Fan Shuaihua Lu Chongyi Ling Qionghua Zhou Jinlan Wang |
| author_sort | Zhilong Song |
| collection | DOAJ |
| description | Abstract Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. Here, we present a framework named Material Generation with Efficient Global Chemical Space Search (MAGECS) that addresses this challenge by integrating the bird swarm algorithm and supervised graph neural networks, enabling effective navigation of generative models in the immense chemical space towards materials with target properties. Applied to the design of alloy electrocatalysts for CO2 reduction (CO2RR), MAGECS generates over 250,000 structures, achieving a 2.5-fold increase in high-activity structures (35%) compared to random generation. Five predicted alloys— CuAl, AlPd, Sn2Pd5, Sn9Pd7, and CuAlSe2 are synthesized and characterized, with two showing around 90% Faraday efficiency for CO2RR. This work highlights the potential of MAGECS to revolutionize functional material development, paving the way for fully automated, artificial intelligence-driven material design. |
| format | Article |
| id | doaj-art-29a781702f6f448cabba6ac5bce6c5f8 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-29a781702f6f448cabba6ac5bce6c5f82025-01-26T12:41:11ZengNature PortfolioNature Communications2041-17232025-01-0116111010.1038/s41467-024-55613-zInverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithmZhilong Song0Linfeng Fan1Shuaihua Lu2Chongyi Ling3Qionghua Zhou4Jinlan Wang5Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityKey Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityKey Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityKey Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityKey Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityKey Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityAbstract Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. Here, we present a framework named Material Generation with Efficient Global Chemical Space Search (MAGECS) that addresses this challenge by integrating the bird swarm algorithm and supervised graph neural networks, enabling effective navigation of generative models in the immense chemical space towards materials with target properties. Applied to the design of alloy electrocatalysts for CO2 reduction (CO2RR), MAGECS generates over 250,000 structures, achieving a 2.5-fold increase in high-activity structures (35%) compared to random generation. Five predicted alloys— CuAl, AlPd, Sn2Pd5, Sn9Pd7, and CuAlSe2 are synthesized and characterized, with two showing around 90% Faraday efficiency for CO2RR. This work highlights the potential of MAGECS to revolutionize functional material development, paving the way for fully automated, artificial intelligence-driven material design.https://doi.org/10.1038/s41467-024-55613-z |
| spellingShingle | Zhilong Song Linfeng Fan Shuaihua Lu Chongyi Ling Qionghua Zhou Jinlan Wang Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm Nature Communications |
| title | Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm |
| title_full | Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm |
| title_fullStr | Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm |
| title_full_unstemmed | Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm |
| title_short | Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm |
| title_sort | inverse design of promising electrocatalysts for co2 reduction via generative models and bird swarm algorithm |
| url | https://doi.org/10.1038/s41467-024-55613-z |
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