Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Toward autonomous design and synthesis of novel inorganic materials
Nathan J. Szymanski, Yan Zeng, Haoyan Huo, et al.
Materials Horizons (2021) Vol. 8, Iss. 8, pp. 2169-2198
Open Access | Times Cited: 107

Showing 1-25 of 107 citing articles:

Recent advances and applications of deep learning methods in materials science
Kamal Choudhary, Brian DeCost, Chi Chen, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 590
High-entropy mechanism to boost ionic conductivity
Yan Zeng, Bin Ouyang, Jue Liu, et al.
Science (2022) Vol. 378, Iss. 6626, pp. 1320-1324
Open Access | Times Cited: 286
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
Revealing the Potential and Challenges of High‐Entropy Layered Cathodes for Sodium‐Based Energy Storage
Hong Gao, Jiayi Li, Fan Zhang, et al.
Advanced Energy Materials (2024) Vol. 14, Iss. 20
Open Access | Times Cited: 48
The rise of high-entropy battery materials
Bin Ouyang, Yan Zeng
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 44
Accelerated chemical science with AI
Seoin Back, Alán Aspuru-Guzik, Michele Ceriotti, et al.
Digital Discovery (2023) Vol. 3, Iss. 1, pp. 23-33
Open Access | Times Cited: 43
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: 33
Bright Innovations: Review of Next-Generation Advances in Scintillator Engineering
Pallavi Singh, G. Dosovitskiy, Yehonadav Bekenstein
ACS Nano (2024) Vol. 18, Iss. 22, pp. 14029-14049
Open Access | Times Cited: 20
Navigating phase diagram complexity to guide robotic inorganic materials synthesis
Jiadong Chen, Samuel R. Cross, Lincoln J. Miara, et al.
Nature Synthesis (2024) Vol. 3, Iss. 5, pp. 606-614
Open Access | Times Cited: 17
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
Machine learning for design principles for single atom catalysts towards electrochemical reactions
Mohsen Tamtaji, Hanyu Gao, Md Delowar Hossain, et al.
Journal of Materials Chemistry A (2022) Vol. 10, Iss. 29, pp. 15309-15331
Open Access | Times Cited: 67
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
Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions
Haoyan Huo, Christopher J. Bartel, Tanjin He, et al.
Chemistry of Materials (2022) Vol. 34, Iss. 16, pp. 7323-7336
Open Access | Times Cited: 45
Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature
Zheren Wang, Olga Kononova, Kevin Cruse, et al.
Scientific Data (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 42
Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature
Tanjin He, Haoyan Huo, Christopher J. Bartel, et al.
Science Advances (2023) Vol. 9, Iss. 23
Open Access | Times Cited: 35
Real-Time Diagnostics of 2D Crystal Transformations by Pulsed Laser Deposition: Controlled Synthesis of Janus WSSe Monolayers and Alloys
Sumner B. Harris, Yu‐Chuan Lin, Alexander A. Puretzky, et al.
ACS Nano (2023) Vol. 17, Iss. 3, pp. 2472-2486
Open Access | Times Cited: 33
Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification
Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 29
Accelerating the prediction of stable materials with machine learning
Sean D. Griesemer, Yi Xia, Chris Wolverton
Nature Computational Science (2023) Vol. 3, Iss. 11, pp. 934-945
Closed Access | Times Cited: 29
Autonomous Synthesis of Thin Film Materials with Pulsed Laser Deposition Enabled by In Situ Spectroscopy and Automation
Sumner B. Harris, Arpan Biswas, Seok Joon Yun, et al.
Small Methods (2024) Vol. 8, Iss. 9
Open Access | Times Cited: 10
Challenges and Strategies for Synthesizing High Performance Micro and Nanoscale High Entropy Oxide Materials
Liang Zhang, Jiru Jia, Jianhua Yan
Small (2024) Vol. 20, Iss. 28
Closed Access | Times Cited: 8
Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification
Nathan J. Szymanski, Sean Fu, Ellen Persson, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 8
Process intensification 4.0: A new approach for attaining new, sustainable and circular processes enabled by machine learning
Enrique A. López-Guajardo, Fernando Delgado‐Licona, A. Alvarez, et al.
Chemical Engineering and Processing - Process Intensification (2021) Vol. 180, pp. 108671-108671
Closed Access | Times Cited: 52
Toward autonomous materials research: Recent progress and future challenges
Joseph H. Montoya, Muratahan Aykol, Abraham Anapolsky, et al.
Applied Physics Reviews (2022) Vol. 9, Iss. 1
Closed Access | Times Cited: 36
Predicting hydrogen storage capacity of V–Ti–Cr–Fe alloy via ensemble machine learning
Ziliang Lu, Jianwei Wang, Yuanfang Wu, et al.
International Journal of Hydrogen Energy (2022) Vol. 47, Iss. 81, pp. 34583-34593
Closed Access | Times Cited: 29
Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery
Fernando Delgado‐Licona, Milad Abolhasani
Advanced Intelligent Systems (2022) Vol. 5, Iss. 4
Open Access | Times Cited: 29
Page 1 - Next Page

Scroll to top