{"title":"人工智能-催化剂管道:加速催化剂创新从实验室走向工业","authors":"Aoming Li, Peng Cui, Xu Wang, Adrian Fisher, Lanyu Li, Daojian Cheng","doi":"10.1007/s11705-025-2560-3","DOIUrl":null,"url":null,"abstract":"<div><p>The integration of high-throughput experimental technologies with artificial intelligence is transforming catalyst research and development. This study explores the synergistic convergence of artificial intelligence and high-throughput experimentation in chemical catalysis, highlighting both current and emerging experimental techniques. It examines how AI-driven methodologies enhance data analysis, automate complex decision-making processes, and optimize catalyst design for industrial applications. The future of research laboratories is envisioned as autonomous, self-driven environments that streamline and accelerate the transition from conceptualization to practical implementation. Key challenges, including data quality, model interpretability, and the scalability of industrial applications, are critically analyzed. Future research should focus on addressing these challenges through strategic methodologies, establishing a systematic framework to fully harness the potential of artificial intelligence and high-throughput experimentation. These advancements will enhance research efficiency and drive innovation in catalysis.\n</p><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":571,"journal":{"name":"Frontiers of Chemical Science and Engineering","volume":"19 7","pages":""},"PeriodicalIF":4.5000,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The artificial intelligence-catalyst pipeline: accelerating catalyst innovation from laboratory to industry\",\"authors\":\"Aoming Li, Peng Cui, Xu Wang, Adrian Fisher, Lanyu Li, Daojian Cheng\",\"doi\":\"10.1007/s11705-025-2560-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The integration of high-throughput experimental technologies with artificial intelligence is transforming catalyst research and development. This study explores the synergistic convergence of artificial intelligence and high-throughput experimentation in chemical catalysis, highlighting both current and emerging experimental techniques. It examines how AI-driven methodologies enhance data analysis, automate complex decision-making processes, and optimize catalyst design for industrial applications. The future of research laboratories is envisioned as autonomous, self-driven environments that streamline and accelerate the transition from conceptualization to practical implementation. Key challenges, including data quality, model interpretability, and the scalability of industrial applications, are critically analyzed. Future research should focus on addressing these challenges through strategic methodologies, establishing a systematic framework to fully harness the potential of artificial intelligence and high-throughput experimentation. These advancements will enhance research efficiency and drive innovation in catalysis.\\n</p><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>\",\"PeriodicalId\":571,\"journal\":{\"name\":\"Frontiers of Chemical Science and Engineering\",\"volume\":\"19 7\",\"pages\":\"\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers of Chemical Science and Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11705-025-2560-3\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers of Chemical Science and Engineering","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s11705-025-2560-3","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
The artificial intelligence-catalyst pipeline: accelerating catalyst innovation from laboratory to industry
The integration of high-throughput experimental technologies with artificial intelligence is transforming catalyst research and development. This study explores the synergistic convergence of artificial intelligence and high-throughput experimentation in chemical catalysis, highlighting both current and emerging experimental techniques. It examines how AI-driven methodologies enhance data analysis, automate complex decision-making processes, and optimize catalyst design for industrial applications. The future of research laboratories is envisioned as autonomous, self-driven environments that streamline and accelerate the transition from conceptualization to practical implementation. Key challenges, including data quality, model interpretability, and the scalability of industrial applications, are critically analyzed. Future research should focus on addressing these challenges through strategic methodologies, establishing a systematic framework to fully harness the potential of artificial intelligence and high-throughput experimentation. These advancements will enhance research efficiency and drive innovation in catalysis.
期刊介绍:
Frontiers of Chemical Science and Engineering presents the latest developments in chemical science and engineering, emphasizing emerging and multidisciplinary fields and international trends in research and development. The journal promotes communication and exchange between scientists all over the world. The contents include original reviews, research papers and short communications. Coverage includes catalysis and reaction engineering, clean energy, functional material, nanotechnology and nanoscience, biomaterials and biotechnology, particle technology and multiphase processing, separation science and technology, sustainable technologies and green processing.