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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

On-the-fly closed-loop materials discovery via Bayesian active learning
A. Gilad Kusne, Heshan Yu, Changming Wu, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 271

Showing 1-25 of 271 citing articles:

Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, et al.
Nature (2023) Vol. 620, Iss. 7972, pp. 47-60
Closed Access | Times Cited: 702
Machine learning for alloys
Gus L. W. Hart, Tim Mueller, Cormac Toher, et al.
Nature Reviews Materials (2021) Vol. 6, Iss. 8, pp. 730-755
Closed Access | Times Cited: 436
Small data machine learning in materials science
Pengcheng Xu, Xiaobo Ji, Minjie Li, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 271
The rise of self-driving labs in chemical and materials sciences
Milad Abolhasani, Eugenia Kumacheva
Nature Synthesis (2023) Vol. 2, Iss. 6, pp. 483-492
Open Access | Times Cited: 257
Autonomous experimentation systems for materials development: A community perspective
Eric A. Stach, Brian DeCost, A. Gilad Kusne, et al.
Matter (2021) Vol. 4, Iss. 9, pp. 2702-2726
Open Access | Times Cited: 253
Accelerating materials discovery using artificial intelligence, high performance computing and robotics
Edward O. Pyzer‐Knapp, Jed W. Pitera, Peter Staar, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 200
Materials, physics and systems for multicaloric cooling
Huilong Hou, Suxin Qian, Ichiro Takeuchi
Nature Reviews Materials (2022) Vol. 7, Iss. 8, pp. 633-652
Closed Access | Times Cited: 172
Machine learning for advanced energy materials
Liu Yun, Oladapo Christopher Esan, Zhefei Pan, et al.
Energy and AI (2021) Vol. 3, pp. 100049-100049
Open Access | Times Cited: 148
Roadmap on Machine learning in electronic structure
Heather J. Kulik, Thomas Hammerschmidt, Jonathan Schmidt, et al.
Electronic Structure (2022) Vol. 4, Iss. 2, pp. 023004-023004
Open Access | Times Cited: 132
Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis
Marcus H. Reis, Filipp Gusev, Nicholas G. Taylor, et al.
Journal of the American Chemical Society (2021) Vol. 143, Iss. 42, pp. 17677-17689
Closed Access | Times Cited: 108
Toward autonomous design and synthesis of novel inorganic materials
Nathan J. Szymanski, Yan Zeng, Haoyan Huo, et al.
Materials Horizons (2021) Vol. 8, Iss. 8, pp. 2169-2198
Open Access | Times Cited: 107
An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials
Y. Jiang, Daniel Salley, Abhishek Sharma, et al.
Science Advances (2022) Vol. 8, Iss. 40
Open Access | Times Cited: 99
Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling
Adarsh Dave, Jared Mitchell, Sven Burke, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 93
Integrated optical memristors
Nathan Youngblood, Carlos Rı́os, Wolfram H. P. Pernice, et al.
Nature Photonics (2023) Vol. 17, Iss. 7, pp. 561-572
Closed Access | Times Cited: 84
Emerging Trends in Machine Learning: A Polymer Perspective
Tyler B. Martin, Debra J. Audus
ACS Polymers Au (2023) Vol. 3, Iss. 3, pp. 239-258
Open Access | Times Cited: 82
Bayesian optimization with active learning of design constraints using an entropy-based approach
Danial Khatamsaz, Brent Vela, Prashant Singh, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 59
Combinatorial synthesis for AI-driven materials discovery
John M. Gregoire, Lan Zhou, Joel A. Haber
Nature Synthesis (2023) Vol. 2, Iss. 6, pp. 493-504
Closed Access | Times Cited: 52
In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science
Joshua Schrier, Alexander J. Norquist, Tonio Buonassisi, et al.
Journal of the American Chemical Society (2023) Vol. 145, Iss. 40, pp. 21699-21716
Open Access | Times Cited: 46
Exploiting redundancy in large materials datasets for efficient machine learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 44
Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization
Liqianyun Xu, Feng Wu, Renjie Chen, et al.
Energy storage materials (2023) Vol. 59, pp. 102785-102785
Closed Access | Times Cited: 41
Self-Driving Laboratories for Chemistry and Materials Science
Gary Tom, Stefan P. Schmid, Sterling G. Baird, et al.
Chemical Reviews (2024) Vol. 124, Iss. 16, pp. 9633-9732
Open Access | Times Cited: 33
Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures
Vera Kuznetsova, Áine Coogan, Dmitry Botov, et al.
Advanced Materials (2024) Vol. 36, Iss. 18
Open Access | Times Cited: 22
Novel nanocomposite-superlattices for low energy and high stability nanoscale phase-change memory
Xiangjin Wu, Asir Intisar Khan, Hengyuan Lee, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 21
Navigating phase diagram complexity to guide robotic inorganic materials synthesis
Jiadong Chen, Samuel R. Cross, Lincoln J. Miara, et al.
Nature Synthesis (2024) Vol. 3, Iss. 5, pp. 606-614
Open Access | Times Cited: 17
ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories
Malcolm Sim, Mohammad Ghazi Vakili, Felix Strieth‐Kalthoff, et al.
Matter (2024) Vol. 7, Iss. 9, pp. 2959-2977
Closed Access | Times Cited: 17
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