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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 26-50 of 196 citing articles:

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra
Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng, et al.
Chemistry of Materials (2021) Vol. 33, Iss. 11, pp. 4204-4215
Open Access | Times Cited: 69
AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles
Xingzhi Wang, Jie Li, Hyun Dong Ha, et al.
JACS Au (2021) Vol. 1, Iss. 3, pp. 316-327
Open Access | Times Cited: 66
The case for data science in experimental chemistry: examples and recommendations
Junko Yano, Kelly J. Gaffney, John M. Gregoire, et al.
Nature Reviews Chemistry (2022) Vol. 6, Iss. 5, pp. 357-370
Open Access | Times Cited: 60
Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings
Shufeng Kong, Francesco Ricci, Dan Guevarra, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 55
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: 54
Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration
Fuzhan Rahmanian, Jackson Flowers, Dan Guevarra, et al.
Advanced Materials Interfaces (2022) Vol. 9, Iss. 8
Open Access | Times Cited: 51
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: 50
Brokering between tenants for an international materials acceleration platform
Monika Vogler, Jonas Busk, Hamidreza Hajiyani, et al.
Matter (2023) Vol. 6, Iss. 9, pp. 2647-2665
Open Access | Times Cited: 28
ET-AL: Entropy-targeted active learning for bias mitigation in materials data
James M. Rondinelli, Wei Chen
Applied Physics Reviews (2023) Vol. 10, Iss. 2
Open Access | Times Cited: 23
Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects
Shanling Ji, Jianxiong Zhu, Yaxin Yang, et al.
Small Methods (2024) Vol. 8, Iss. 7
Closed Access | Times Cited: 14
A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments
Arpan Biswas, Yongtao Liu, Nicole Creange, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 13
The future of self-driving laboratories: from human in the loop interactive AI to gamification
Holland Hysmith, Elham Foadian, Shakti P. Padhy, et al.
Digital Discovery (2024) Vol. 3, Iss. 4, pp. 621-636
Open Access | Times Cited: 13
Designing workflows for materials characterization
Sergei V. Kalinin, Maxim Ziatdinov, Mahshid Ahmadi, et al.
Applied Physics Reviews (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 11
Accelerated discovery of nanostructured high-entropy and multicomponent alloys via high-throughput strategies
Changjun Cheng, Yu Zou
Progress in Materials Science (2025), pp. 101429-101429
Open Access | Times Cited: 1
Artificial intelligence interventions in 2D MXenes-based photocatalytic applications
Durga Madhab Mahapatra, Ashish Kumar, Rajesh Kumar, et al.
Coordination Chemistry Reviews (2025) Vol. 529, pp. 216460-216460
Closed Access | Times Cited: 1
Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis
Christoph Scheurer, Karsten Reuter
Nature Catalysis (2025) Vol. 8, Iss. 1, pp. 13-19
Closed Access | Times Cited: 1
Can we predict materials that can be synthesised?
Filip Szczypiński, Steven Bennett, Kim E. Jelfs
Chemical Science (2020) Vol. 12, Iss. 3, pp. 830-840
Open Access | Times Cited: 61
Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams
Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, et al.
Science Advances (2021) Vol. 7, Iss. 51
Open Access | Times Cited: 49
Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries
Arghya Bhowmik, Maitane Berecibar, Montse Casas‐Cabanas, et al.
Advanced Energy Materials (2021) Vol. 12, Iss. 17
Open Access | Times Cited: 46
Deep learning for visualization and novelty detection in large X-ray diffraction datasets
Lars Banko, Phillip M. Maffettone, Dennis Naujoks, et al.
npj Computational Materials (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 43
From materials discovery to system optimization by integrating combinatorial electrochemistry and data science
Helge S. Stein, Alexey O. Sanin, Fuzhan Rahmanian, et al.
Current Opinion in Electrochemistry (2022) Vol. 35, pp. 101053-101053
Closed Access | Times Cited: 37
On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning
Austin McDannald, Matthias Frontzek, A. T. Savici, et al.
Applied Physics Reviews (2022) Vol. 9, Iss. 2, pp. 021408-021408
Open Access | Times Cited: 34
Atlas: A Brain for Self-driving Laboratories
Riley J. Hickman, Malcolm Sim, Sergio Pablo‐García, et al.
(2023)
Open Access | Times Cited: 20
Orchestrating nimble experiments across interconnected labs
Dan Guevarra, Kevin Kan, Yungchieh Lai, et al.
Digital Discovery (2023) Vol. 2, Iss. 6, pp. 1806-1812
Open Access | Times Cited: 20
Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence
Jonathan Zaslavsky, Pauric Bannigan, Christine Allen
Expert Opinion on Drug Delivery (2023) Vol. 20, Iss. 2, pp. 241-257
Closed Access | Times Cited: 17

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