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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:

Machine Learning for Chemical Reactivity: The Importance of Failed Experiments
Felix Strieth‐Kalthoff, Frederik Sandfort, Marius Kühnemund, et al.
Angewandte Chemie International Edition (2022) Vol. 61, Iss. 29
Closed Access | Times Cited: 148

Showing 1-25 of 148 citing articles:

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab
Martin Seifrid, Robert Pollice, Andrés Aguilar-Gránda, et al.
Accounts of Chemical Research (2022) Vol. 55, Iss. 17, pp. 2454-2466
Open Access | Times Cited: 147
Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling
Nicholas H. Angello, Vandana Rathore, Wiktor Beker, et al.
Science (2022) Vol. 378, Iss. 6618, pp. 399-405
Closed Access | Times Cited: 127
Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
Zhengkai Tu, Thijs Stuyver, Connor W. Coley
Chemical Science (2022) Vol. 14, Iss. 2, pp. 226-244
Open Access | Times Cited: 76
On the use of real-world datasets for reaction yield prediction
Mandana Saebi, Bozhao Nan, John E. Herr, et al.
Chemical Science (2023) Vol. 14, Iss. 19, pp. 4997-5005
Open Access | Times Cited: 68
Recent applications of machine learning in alloy design: A review
Mingwei Hu, Qiyang Tan, Ruth Knibbe, et al.
Materials Science and Engineering R Reports (2023) Vol. 155, pp. 100746-100746
Closed Access | Times Cited: 57
Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte
Jin Li, Meisa Zhou, Hong‐Hui Wu, et al.
Advanced Energy Materials (2024) Vol. 14, Iss. 20
Closed Access | Times Cited: 48
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: 35
Palladium-catalyzed decarboxylative (4 + 3) cycloadditions of bicyclobutanes with 2-alkylidenetrimethylene carbonates for the synthesis of 2-oxabicyclo[4.1.1]octanes
X. N. Gao, Lei Tang, Xu Zhang, et al.
Chemical Science (2024) Vol. 15, Iss. 34, pp. 13942-13948
Open Access | Times Cited: 23
Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings
Jules Schleinitz, Maxime Langevin, Yanis Smail, et al.
Journal of the American Chemical Society (2022) Vol. 144, Iss. 32, pp. 14722-14730
Open Access | Times Cited: 53
Controlled Synthesis of Multicolor Carbon Dots Assisted by Machine Learning
Jiao Chen, Jun Luo, M. Hu, et al.
Advanced Functional Materials (2022) Vol. 33, Iss. 2
Closed Access | Times Cited: 42
The value of negative results in data-driven catalysis research
Toshiaki Taniike, Keisuke Takahashi
Nature Catalysis (2023) Vol. 6, Iss. 2, pp. 108-111
Closed Access | Times Cited: 40
Dataset Design for Building Models of Chemical Reactivity
Priyanka Raghavan, Brittany C. Haas, Madeline E. Ruos, et al.
ACS Central Science (2023) Vol. 9, Iss. 12, pp. 2196-2204
Open Access | Times Cited: 36
Machine Learning C–N Couplings: Obstacles for a General-Purpose Reaction Yield Prediction
Martin Fitzner, Georg Wuitschik, Raffael Koller, et al.
ACS Omega (2023) Vol. 8, Iss. 3, pp. 3017-3025
Open Access | Times Cited: 35
Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation
Lucas Foppa, Frederik Rüther, Michael Geske, et al.
Journal of the American Chemical Society (2023) Vol. 145, Iss. 6, pp. 3427-3442
Open Access | Times Cited: 32
Functional Group Evaluation Kit for Digitalization of Information on the Functional Group Compatibility and Chemoselectivity of Organic Reactions
Natsuki Saito, Anna Nawachi, Yuta Kondo, et al.
Bulletin of the Chemical Society of Japan (2023) Vol. 96, Iss. 5, pp. 465-474
Closed Access | Times Cited: 26
Negative Data in Data Sets for Machine Learning Training
Michael P. Maloney, Connor W. Coley, Samuel Genheden, et al.
The Journal of Organic Chemistry (2023) Vol. 88, Iss. 9, pp. 5239-5241
Closed Access | Times Cited: 24
Autonomous optimization of an organic solar cell in a 4-dimensional parameter space
Tobias Osterrieder, F. Schmitt, Larry Lüer, et al.
Energy & Environmental Science (2023) Vol. 16, Iss. 9, pp. 3984-3993
Open Access | Times Cited: 24
Negative Data in Data Sets for Machine Learning Training
Michael P. Maloney, Connor W. Coley, Samuel Genheden, et al.
Organic Letters (2023) Vol. 25, Iss. 17, pp. 2945-2947
Closed Access | Times Cited: 23
Probing the chemical ‘reactome’ with high-throughput experimentation data
Emma King‐Smith, Simon Berritt, Louise Bernier, et al.
Nature Chemistry (2024) Vol. 16, Iss. 4, pp. 633-643
Open Access | Times Cited: 15
Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis
Toshiaki Taniike, Aya Fujiwara, Sunao Nakanowatari, et al.
Communications Chemistry (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 15
Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge
Felix Strieth‐Kalthoff, Sara Szymkuć, Karol Molga, et al.
Journal of the American Chemical Society (2024)
Closed Access | Times Cited: 14
Paving the road towards automated homogeneous catalyst design
Adarsh V. Kalikadien, A.H. Mirza, Aydin Najl Hossaini, et al.
ChemPlusChem (2024) Vol. 89, Iss. 7
Open Access | Times Cited: 12
Understanding Hot Injection Quantum Dot Synthesis Outcomes Using Automated High-Throughput Experiment Platforms and Machine Learning
Rui Xu, Logan P. Keating, Ajit Vikram, et al.
Chemistry of Materials (2024) Vol. 36, Iss. 3, pp. 1513-1525
Closed Access | Times Cited: 11
AI for organic and polymer synthesis
Hong Xin, Qi Yang, Kuangbiao Liao, et al.
Science China Chemistry (2024) Vol. 67, Iss. 8, pp. 2461-2496
Closed Access | Times Cited: 9
Bridging the information gap in organic chemical reactions
Malte L. Schrader, Felix Schäfer, Felix Schäfers, et al.
Nature Chemistry (2024) Vol. 16, Iss. 4, pp. 491-498
Closed Access | Times Cited: 8
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