Active learning streamlines development of high performance catalysts for higher alcohol synthesis
Author
Abstract
Suggested Citation
DOI: 10.1038/s41467-024-50215-1
Download full text from publisher
References listed on IDEAS
- Miao Zhong & Kevin Tran & Yimeng Min & Chuanhao Wang & Ziyun Wang & Cao-Thang Dinh & Phil De Luna & Zongqian Yu & Armin Sedighian Rasouli & Peter Brodersen & Song Sun & Oleksandr Voznyy & Chih-Shan Ta, 2020. "Accelerated discovery of CO2 electrocatalysts using active machine learning," Nature, Nature, vol. 581(7807), pages 178-183, May.
- Yinwen Li & Wa Gao & Mi Peng & Junbo Zhang & Jialve Sun & Yao Xu & Song Hong & Xi Liu & Xingwu Liu & Min Wei & Bingsen Zhang & Ding Ma, 2020. "Interfacial Fe5C2-Cu catalysts toward low-pressure syngas conversion to long-chain alcohols," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
- Benjamin J. Shields & Jason Stevens & Jun Li & Marvin Parasram & Farhan Damani & Jesus I. Martinez Alvarado & Jacob M. Janey & Ryan P. Adams & Abigail G. Doyle, 2021. "Bayesian reaction optimization as a tool for chemical synthesis," Nature, Nature, vol. 590(7844), pages 89-96, February.
- Benjamin P. MacLeod & Fraser G. L. Parlane & Connor C. Rupnow & Kevan E. Dettelbach & Michael S. Elliott & Thomas D. Morrissey & Ted H. Haley & Oleksii Proskurin & Michael B. Rooney & Nina Taherimakhs, 2022. "A self-driving laboratory advances the Pareto front for material properties," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- Christian Kunkel & Johannes T. Margraf & Ke Chen & Harald Oberhofer & Karsten Reuter, 2021. "Active discovery of organic semiconductors," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
- Yizhi Xiang & Libor Kovarik & Norbert Kruse, 2019. "Rate and selectivity hysteresis during the carbon monoxide hydrogenation over promoted Co/MnOx catalysts," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
- Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
- Lorillee Tallorin & JiaLei Wang & Woojoo E. Kim & Swagat Sahu & Nicolas M. Kosa & Pu Yang & Matthew Thompson & Michael K. Gilson & Peter I. Frazier & Michael D. Burkart & Nathan C. Gianneschi, 2018. "Discovering de novo peptide substrates for enzymes using machine learning," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
- Gang Wang & Shinya Mine & Duotian Chen & Yuan Jing & Kah Wei Ting & Taichi Yamaguchi & Motoshi Takao & Zen Maeno & Ichigaku Takigawa & Koichi Matsushita & Ken-ichi Shimizu & Takashi Toyao, 2023. "Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
- Kevin Maik Jablonka & Giriprasad Melpatti Jothiappan & Shefang Wang & Berend Smit & Brian Yoo, 2021. "Bias free multiobjective active learning for materials design and discovery," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Zhiyuan Han & An Chen & Zejian Li & Mengtian Zhang & Zhilong Wang & Lixue Yang & Runhua Gao & Yeyang Jia & Guanjun Ji & Zhoujie Lao & Xiao Xiao & Kehao Tao & Jing Gao & Wei Lv & Tianshuai Wang & Jinji, 2024. "Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
- Adarsh Dave & Jared Mitchell & Sven Burke & Hongyi Lin & Jay Whitacre & Venkatasubramanian Viswanathan, 2022. "Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
- Gang Wang & Shinya Mine & Duotian Chen & Yuan Jing & Kah Wei Ting & Taichi Yamaguchi & Motoshi Takao & Zen Maeno & Ichigaku Takigawa & Koichi Matsushita & Ken-ichi Shimizu & Takashi Toyao, 2023. "Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
- Zhilong Song & Linfeng Fan & Shuaihua Lu & Chongyi Ling & Qionghua Zhou & Jinlan Wang, 2025. "Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
- Kangming Li & Daniel Persaud & Kamal Choudhary & Brian DeCost & Michael Greenwood & Jason Hattrick-Simpers, 2023. "Exploiting redundancy in large materials datasets for efficient machine learning with less data," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
- Yilei Wu & Chang-Feng Wang & Ming-Gang Ju & Qiangqiang Jia & Qionghua Zhou & Shuaihua Lu & Xinying Gao & Yi Zhang & Jinlan Wang, 2024. "Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
- Snehi Shrestha & Kieran James Barvenik & Tianle Chen & Haochen Yang & Yang Li & Meera Muthachi Kesavan & Joshua M. Little & Hayden C. Whitley & Zi Teng & Yaguang Luo & Eleonora Tubaldi & Po-Yen Chen, 2024. "Machine intelligence accelerated design of conductive MXene aerogels with programmable properties," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
- Kihoon Bang & Doosun Hong & Youngtae Park & Donghun Kim & Sang Soo Han & Hyuck Mo Lee, 2023. "Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
- Han Li & Ruotian Zhang & Yaosen Min & Dacheng Ma & Dan Zhao & Jianyang Zeng, 2023. "A knowledge-guided pre-training framework for improving molecular representation learning," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
- Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
- Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
- Fei Qian & Jiawei Bai & Yi Cai & Hui Yang & Xue-Min Cao & Xingchen Liu & Xing-Wu Liu & Yong Yang & Yong-Wang Li & Ding Ma & Xiao-Dong Wen, 2024. "Stabilized ε-Fe2C catalyst with Mn tuning to suppress C1 byproduct selectivity for high-temperature olefin synthesis," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
- Xinyu Chen & Shuaihua Lu & Qian Chen & Qionghua Zhou & Jinlan Wang, 2024. "From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
- Niklas W. A. Gebauer & Michael Gastegger & Stefaan S. P. Hessmann & Klaus-Robert Müller & Kristof T. Schütt, 2022. "Inverse design of 3d molecular structures with conditional generative neural networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- Yong Zhang & Feifei Chen & Xinyi Yang & Yiran Guo & Xinghua Zhang & Hong Dong & Weihua Wang & Feng Lu & Zunming Lu & Hui Liu & Hui Liu & Yao Xiao & Yahui Cheng, 2025. "Electronic metal-support interaction modulates Cu electronic structures for CO2 electroreduction to desired products," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
- Jiaqi Zhao & Jinjia Liu & Zhenhua Li & Kaiwen Wang & Run Shi & Pu Wang & Qing Wang & Geoffrey I. N. Waterhouse & Xiaodong Wen & Tierui Zhang, 2023. "Ruthenium-cobalt single atom alloy for CO photo-hydrogenation to liquid fuels at ambient pressures," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
- Jikai Sun & Rui Tu & Yuchun Xu & Hongyan Yang & Tie Yu & Dong Zhai & Xiuqin Ci & Weiqiao Deng, 2024. "Machine learning aided design of single-atom alloy catalysts for methane cracking," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
- Huziel E. Sauceda & Luis E. Gálvez-González & Stefan Chmiela & Lauro Oliver Paz-Borbón & Klaus-Robert Müller & Alexandre Tkatchenko, 2022. "BIGDML—Towards accurate quantum machine learning force fields for materials," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
- Sukriti Manna & Troy D. Loeffler & Rohit Batra & Suvo Banik & Henry Chan & Bilvin Varughese & Kiran Sasikumar & Michael Sternberg & Tom Peterka & Mathew J. Cherukara & Stephen K. Gray & Bobby G. Sumpt, 2022. "Learning in continuous action space for developing high dimensional potential energy models," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- Alexander E. Siemenn & Eunice Aissi & Fang Sheng & Armi Tiihonen & Hamide Kavak & Basita Das & Tonio Buonassisi, 2024. "Using scalable computer vision to automate high-throughput semiconductor characterization," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50215-1. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.