Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory
Abstract The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is gen...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-44236-5 |
| _version_ | 1827388333735542784 |
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| author | Yilei Wu Chang-Feng Wang Ming-Gang Ju Qiangqiang Jia Qionghua Zhou Shuaihua Lu Xinying Gao Yi Zhang Jinlan Wang |
| author_facet | Yilei Wu Chang-Feng Wang Ming-Gang Ju Qiangqiang Jia Qionghua Zhou Shuaihua Lu Xinying Gao Yi Zhang Jinlan Wang |
| author_sort | Yilei Wu |
| collection | DOAJ |
| description | Abstract The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is generally much more complex than property and structure prediction, and very few computational predictions are experimentally realized. To solve these challenges, a universal framework that integrates high-throughput experiments, a priori knowledge of chemistry, and ML techniques such as subgroup discovery and support vector machine is proposed to guide the experimental synthesis of materials, which is capable of disclosing structure-property relationship hidden in high-throughput experiments and rapidly screening out materials with high synthesis feasibility from vast chemical space. Through application of our approach to challenging and consequential synthesis problem of 2D silver/bismuth organic-inorganic hybrid perovskites, we have increased the success rate of the synthesis feasibility by a factor of four relative to traditional approaches. This study provides a practical route for solving multidimensional chemical acceleration problems with small dataset from typical laboratory with limited experimental resources available. |
| first_indexed | 2024-03-08T16:16:21Z |
| format | Article |
| id | doaj.art-6fed056a65d84e40a3f6f3e50a8b3988 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-08T16:16:21Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-6fed056a65d84e40a3f6f3e50a8b39882024-01-07T12:33:33ZengNature PortfolioNature Communications2041-17232024-01-0115111010.1038/s41467-023-44236-5Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratoryYilei Wu0Chang-Feng Wang1Ming-Gang Ju2Qiangqiang Jia3Qionghua Zhou4Shuaihua Lu5Xinying Gao6Yi Zhang7Jinlan Wang8Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityInstitute for Science and Applications of Molecular Ferroelectrics, Key Laboratory of the Ministry of Education for Advanced Catalysis Materials, Zhejiang Normal UniversityKey Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityInstitute for Science and Applications of Molecular Ferroelectrics, Key Laboratory of the Ministry of Education for Advanced Catalysis Materials, Zhejiang Normal 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 UniversityInstitute for Science and Applications of Molecular Ferroelectrics, Key Laboratory of the Ministry of Education for Advanced Catalysis Materials, Zhejiang Normal UniversityKey Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityAbstract The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is generally much more complex than property and structure prediction, and very few computational predictions are experimentally realized. To solve these challenges, a universal framework that integrates high-throughput experiments, a priori knowledge of chemistry, and ML techniques such as subgroup discovery and support vector machine is proposed to guide the experimental synthesis of materials, which is capable of disclosing structure-property relationship hidden in high-throughput experiments and rapidly screening out materials with high synthesis feasibility from vast chemical space. Through application of our approach to challenging and consequential synthesis problem of 2D silver/bismuth organic-inorganic hybrid perovskites, we have increased the success rate of the synthesis feasibility by a factor of four relative to traditional approaches. This study provides a practical route for solving multidimensional chemical acceleration problems with small dataset from typical laboratory with limited experimental resources available.https://doi.org/10.1038/s41467-023-44236-5 |
| spellingShingle | Yilei Wu Chang-Feng Wang Ming-Gang Ju Qiangqiang Jia Qionghua Zhou Shuaihua Lu Xinying Gao Yi Zhang Jinlan Wang Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory Nature Communications |
| title | Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory |
| title_full | Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory |
| title_fullStr | Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory |
| title_full_unstemmed | Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory |
| title_short | Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory |
| title_sort | universal machine learning aided synthesis approach of two dimensional perovskites in a typical laboratory |
| url | https://doi.org/10.1038/s41467-023-44236-5 |
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