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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:

Bayesian optimization with known experimental and design constraints for chemistry applications
Riley J. Hickman, Matteo Aldeghi, Florian Häse, et al.
Digital Discovery (2022) Vol. 1, Iss. 5, pp. 732-744
Open Access | Times Cited: 48

Showing 1-25 of 48 citing articles:

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: 51
Self-Driving Laboratory for Polymer Electronics
Aikaterini Vriza, Henry Chan, Jie Xu
Chemistry of Materials (2023) Vol. 35, Iss. 8, pp. 3046-3056
Closed Access | Times Cited: 44
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
Yunchao Xie, Kianoosh Sattari, Chi Zhang, et al.
Progress in Materials Science (2022) Vol. 132, pp. 101043-101043
Open Access | Times Cited: 52
Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning
John H. Dunlap, Jeffrey G. Ethier, Amelia A. Putnam‐Neeb, et al.
Chemical Science (2023) Vol. 14, Iss. 30, pp. 8061-8069
Open Access | Times Cited: 27
Atlas: A Brain for Self-driving Laboratories
Riley J. Hickman, Malcolm Sim, Sergio Pablo‐García, et al.
(2023)
Open Access | Times Cited: 20
Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept
Stanley Lo, Sterling G. Baird, Joshua Schrier, et al.
Digital Discovery (2024) Vol. 3, Iss. 5, pp. 842-868
Open Access | Times Cited: 9
Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs
Kai Yuan Andre Low, Flore Mekki‐Berrada, Abhishek Gupta, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 8
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design
Jorge Benavides-Hernández, Franck Dumeignil
ACS Catalysis (2024) Vol. 14, Iss. 15, pp. 11749-11779
Closed Access | Times Cited: 7
What is a minimal working example for a self-driving laboratory?
Sterling G. Baird, Taylor D. Sparks
Matter (2022) Vol. 5, Iss. 12, pp. 4170-4178
Open Access | Times Cited: 27
Bayesian Optimization as a Flexible and Efficient Design Framework for Sustainable Process Systems
Joel A. Paulson, Calvin Tsay
Current Opinion in Green and Sustainable Chemistry (2024), pp. 100983-100983
Open Access | Times Cited: 4
A Chemist’s Guide to Multi-Objective Optimization Solvers for Reaction Optimization
Aravind Senthil Vel, Daniel Cortés‐Borda, François‐Xavier Felpin
Reaction Chemistry & Engineering (2024) Vol. 9, Iss. 11, pp. 2882-2891
Closed Access | Times Cited: 3
Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation
Rodrigo A. Vargas–Hernández, Kjell Jorner, Robert Pollice, et al.
The Journal of Chemical Physics (2023) Vol. 158, Iss. 10
Open Access | Times Cited: 10
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
Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead!
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, et al.
Engineering (2023) Vol. 27, pp. 23-30
Open Access | Times Cited: 9
Genetic Algorithms for the Discovery of Homogeneous Catalysts
Simone Gallarati, Puck van Gerwen, Alexandre A. Schoepfer, et al.
CHIMIA International Journal for Chemistry (2023) Vol. 77, Iss. 1/2, pp. 39-39
Open Access | Times Cited: 8
Meta optimization based on real-time benchmarking of multiple surrogate models for autonomous flow synthesis
Amirreza Mottafegh, Gwang‐Noh Ahn, Dong‐Pyo Kim
Lab on a Chip (2023) Vol. 23, Iss. 6, pp. 1613-1621
Closed Access | Times Cited: 7
How to Accelerate R&D and Optimize Experiment Planning with Machine Learning and Data Science
Daniel Pacheco Gutiérrez, Linnea M. Folkmann, Hermann Tribukait, et al.
CHIMIA International Journal for Chemistry (2023) Vol. 77, Iss. 1/2, pp. 7-7
Open Access | Times Cited: 7
Compactness matters: Improving Bayesian optimization efficiency of materials formulations through invariant search spaces
Sterling G. Baird, Jason R. Hall, Taylor D. Sparks
Computational Materials Science (2023) Vol. 224, pp. 112134-112134
Open Access | Times Cited: 6
CAPBO: A cost‐aware parallelized Bayesian optimization method for chemical reaction optimization
Runzhe Liang, Haoyang Hu, Yueheng Han, et al.
AIChE Journal (2023) Vol. 70, Iss. 3
Closed Access | Times Cited: 5
The decade of artificial intelligence in chemistry and materials
Alán Aspuru‐Guzik
Digital Discovery (2023) Vol. 2, Iss. 1, pp. 10-11
Open Access | Times Cited: 5
Targeted materials discovery using Bayesian algorithm execution
Sathya R. Chitturi, Akash Ramdas, Yue Wu, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 1
Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation
Riley J. Hickman, Matteo Aldeghi, Alán Aspuru‐Guzik
(2023)
Open Access | Times Cited: 4
Improved decision making with similarity based machine learning: applications in chemistry
Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Machine Learning Science and Technology (2023) Vol. 4, Iss. 4, pp. 045043-045043
Open Access | Times Cited: 4
Bayesian optimization in continuous spaces via virtual process embeddings
Mani Valleti, Rama K. Vasudevan, Maxim Ziatdinov, et al.
Digital Discovery (2022) Vol. 1, Iss. 6, pp. 910-925
Open Access | Times Cited: 7
Build instructions for Closed-loop Spectroscopy Lab: Light-mixing Demo
Sterling G. Baird, Taylor D. Sparks
(2023)
Open Access | Times Cited: 3
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