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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:

Progress and prospects for accelerating materials science with automated and autonomous workflows
Helge S. Stein, John M. Gregoire
Chemical Science (2019) Vol. 10, Iss. 42, pp. 9640-9649
Open Access | Times Cited: 196

Showing 1-25 of 196 citing articles:

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
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
Rechargeable Batteries of the Future—The State of the Art from a BATTERY 2030+ Perspective
Maximilian Fichtner, Kristina Edström, Elixabete Ayerbe, et al.
Advanced Energy Materials (2021) Vol. 12, Iss. 17
Open Access | Times Cited: 238
Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature
Leigh Weston, Vahe Tshitoyan, John Dagdelen, et al.
Journal of Chemical Information and Modeling (2019) Vol. 59, Iss. 9, pp. 3692-3702
Open Access | Times Cited: 206
Data-science driven autonomous process optimization
Melodie Christensen, Lars P. E. Yunker, Folarin Adedeji, et al.
Communications Chemistry (2021) Vol. 4, Iss. 1
Open Access | Times Cited: 175
Generative Models as an Emerging Paradigm in the Chemical Sciences
Dylan M. Anstine, Olexandr Isayev
Journal of the American Chemical Society (2023) Vol. 145, Iss. 16, pp. 8736-8750
Open Access | Times Cited: 160
A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+
Julia Amici, Pietro Asinari, Elixabete Ayerbe, et al.
Advanced Energy Materials (2022) Vol. 12, Iss. 17
Open Access | Times Cited: 138
A self-driving laboratory advances the Pareto front for material properties
Benjamin P. MacLeod, Fraser G. L. Parlane, Connor C. Rupnow, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 118
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge
Florian Häse, Matteo Aldeghi, Riley J. Hickman, et al.
Applied Physics Reviews (2021) Vol. 8, Iss. 3
Open Access | Times Cited: 104
Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
Jiayu Peng, Daniel Schwalbe‐Koda, Karthik Akkiraju, et al.
Nature Reviews Materials (2022) Vol. 7, Iss. 12, pp. 991-1009
Closed Access | Times Cited: 89
Machine learning in nuclear materials research
Dane Morgan, Ghanshyam Pilania, Adrien Couet, et al.
Current Opinion in Solid State and Materials Science (2022) Vol. 26, Iss. 2, pp. 100975-100975
Open Access | Times Cited: 75
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
Revolutionizing drug formulation development: The increasing impact of machine learning
Zeqing Bao, Jack Bufton, Riley J. Hickman, et al.
Advanced Drug Delivery Reviews (2023) Vol. 202, pp. 115108-115108
Closed Access | Times Cited: 52
Self-driving laboratories: A paradigm shift in nanomedicine development
Riley J. Hickman, Pauric Bannigan, Zeqing Bao, et al.
Matter (2023) Vol. 6, Iss. 4, pp. 1071-1081
Open Access | Times Cited: 41
A dynamic knowledge graph approach to distributed self-driving laboratories
Jiaru Bai, Sebastian Mosbach, Connor J. Taylor, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 21
Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics
Ying Shang, Ziyu Xiong, Kang An, et al.
Materials Genome Engineering Advances (2024) Vol. 2, Iss. 1
Open Access | Times Cited: 16
Materials Acceleration Platforms: On the way to autonomous experimentation
Martha M. Flores‐Leonar, L.M. Mejía-Mendoza, Andrés Aguilar-Gránda, et al.
Current Opinion in Green and Sustainable Chemistry (2020) Vol. 25, pp. 100370-100370
Open Access | Times Cited: 128
Benchmarking the acceleration of materials discovery by sequential learning
Brian A. Rohr, Helge S. Stein, Dan Guevarra, et al.
Chemical Science (2020) Vol. 11, Iss. 10, pp. 2696-2706
Open Access | Times Cited: 117
Role of Artificial Intelligence and Machine Learning in Nanosafety
David A. Winkler
Small (2020) Vol. 16, Iss. 36
Open Access | Times Cited: 113
Designing and understanding light-harvesting devices with machine learning
Florian Häse, Loı̈c M. Roch, Pascal Friederich, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 97
Accelerating organic solar cell material's discovery: high-throughput screening and big data
Xabier Rodríguez‐Martínez, Enrique Pascual‐San‐José, Mariano Campoy‐Quiles
Energy & Environmental Science (2021) Vol. 14, Iss. 6, pp. 3301-3322
Open Access | Times Cited: 84
Nobel Turing Challenge: creating the engine for scientific discovery
Hiroaki Kitano
npj Systems Biology and Applications (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 77
High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications
Qingsong Wang, Leonardo Velasco, Ben Breitung, et al.
Advanced Energy Materials (2021) Vol. 11, Iss. 47
Open Access | Times Cited: 76
Flexible automation accelerates materials discovery
Benjamin P. MacLeod, Fraser G. L. Parlane, Amanda K. Brown, et al.
Nature Materials (2021) Vol. 21, Iss. 7, pp. 722-726
Closed Access | Times Cited: 71
Olympus: a benchmarking framework for noisy optimization and experiment planning
Florian Häse, Matteo Aldeghi, Riley J. Hickman, et al.
Machine Learning Science and Technology (2021) Vol. 2, Iss. 3, pp. 035021-035021
Open Access | Times Cited: 70
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