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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:

Principled Exploration of Bipyridine and Terpyridine Additives to Promote Methylammonium Lead Iodide Perovskite Crystallization
Noor Titan Putri Hartono, Mansoor Ani Najeeb, Zhi Li, et al.
Crystal Growth & Design (2022) Vol. 22, Iss. 9, pp. 5424-5431
Closed Access | Times Cited: 11

Showing 11 citing articles:

Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
Yiming Liu, Xinyu Tan, Jie Liang, et al.
Advanced Functional Materials (2023) Vol. 33, Iss. 17
Closed Access | Times Cited: 80
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
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
Atlas: A Brain for Self-driving Laboratories
Riley J. Hickman, Malcolm Sim, Sergio Pablo‐García, et al.
(2023)
Open Access | Times Cited: 20
The role of machine learning in perovskite solar cell research
Chen Chen, Ayman Maqsood, T. Jesper Jacobsson
Journal of Alloys and Compounds (2023) Vol. 960, pp. 170824-170824
Closed Access | Times Cited: 19
Olympus, enhanced: benchmarking mixed-parameter and multi-objective optimization in chemistry and materials science
Riley J. Hickman, P.N. Parakh, Austin Cheng, et al.
(2023)
Open Access | Times Cited: 9
The chemistry of the s- and p-block elements with 2,2′:6′,2′′-terpyridine ligands
Andreas Winter, George R. Newkome, Ulrich S. Schubert
Inorganic Chemistry Frontiers (2023) Vol. 11, Iss. 2, pp. 342-399
Closed Access | Times Cited: 7
Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation
Riley J. Hickman, Matteo Aldeghi, Alán Aspuru‐Guzik
(2023)
Open Access | Times Cited: 5
Rediscovering the Particle-in-a-Box: Machine Learning Regression Analysis for Hypothesis Generation in Physical Chemistry Lab
Elizabeth S. Thrall, Fernando Martinez Lopez, Thomas Egg, et al.
Journal of Chemical Education (2023) Vol. 100, Iss. 12, pp. 4933-4940
Closed Access | Times Cited: 3
Machine learning-assisted design of high-performance perovskite photodetectors: a review
Xiaohui Li, Yongxiang Mai, Chunfeng Lan, et al.
Advanced Composites and Hybrid Materials (2024) Vol. 8, Iss. 1
Closed Access
Rediscovering the particle in a box: Machine learning regression analysis for hypothesis generation in physical chemistry lab
Elizabeth S. Thrall, Fernando Martinez Lopez, Thomas Egg, et al.
(2023)
Open Access | Times Cited: 1
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