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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:

The Role of Machine Learning in the Understanding and Design of Materials
Seyed Mohamad Moosavi, Kevin Maik Jablonka, Berend Smit
Journal of the American Chemical Society (2020) Vol. 142, Iss. 48, pp. 20273-20287
Open Access | Times Cited: 284

Showing 1-25 of 284 citing articles:

Artificial Intelligence‐Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin
Zixuan Zhang, Feng Wen, Zhongda Sun, et al.
Advanced Intelligent Systems (2022) Vol. 4, Iss. 7
Open Access | Times Cited: 282
Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review
Yongliang Yan, Tohid N. Borhani, Sai Gokul Subraveti, et al.
Energy & Environmental Science (2021) Vol. 14, Iss. 12, pp. 6122-6157
Open Access | Times Cited: 167
Machine learning for advanced energy materials
Liu Yun, Oladapo Christopher Esan, Zhefei Pan, et al.
Energy and AI (2021) Vol. 3, pp. 100049-100049
Open Access | Times Cited: 143
Generative Models as an Emerging Paradigm in the Chemical Sciences
Dylan M. Anstine, Olexandr Isayev
Journal of the American Chemical Society (2023) Vol. 145, Iss. 16, pp. 8736-8750
Open Access | Times Cited: 142
Understanding ligand-protected noble metal nanoclusters at work
María Francisca Matus, Hannu Häkkinen
Nature Reviews Materials (2023) Vol. 8, Iss. 6, pp. 372-389
Closed Access | Times Cited: 132
Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale
Xiang Chen, Xinyan Liu, Xin Shen, et al.
Angewandte Chemie International Edition (2021) Vol. 60, Iss. 46, pp. 24354-24366
Closed Access | Times Cited: 112
Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?
Watcharop Chaikittisilp, Yusuke Yamauchi, Katsuhiko Ariga
Advanced Materials (2021) Vol. 34, Iss. 7
Closed Access | Times Cited: 112
14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali, et al.
Digital Discovery (2023) Vol. 2, Iss. 5, pp. 1233-1250
Open Access | Times Cited: 106
Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction
Ying Guo, Xinru He, Yuming Su, et al.
Journal of the American Chemical Society (2021) Vol. 143, Iss. 15, pp. 5755-5762
Closed Access | Times Cited: 105
Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios
Xu Chen, Ba Trung Cao, Yong Yuan, et al.
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 405, pp. 115852-115852
Open Access | Times Cited: 97
Emerging Strategies for CO2 Photoreduction to CH4: From Experimental to Data‐Driven Design
Shuwen Cheng, Zhehao Sun, Kang Hui Lim, et al.
Advanced Energy Materials (2022) Vol. 12, Iss. 20
Closed Access | Times Cited: 94
Carbon dioxide separation and capture by adsorption: a review
Mohsen Karimi, Mohammad Shirzad, José A. C. Silva, et al.
Environmental Chemistry Letters (2023) Vol. 21, Iss. 4, pp. 2041-2084
Open Access | Times Cited: 91
High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration
Andrew Rosen, Victor Fung, Patrick Huck, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 89
Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning
Jianwen Liu, Wenzhi Luo, Lei Wang, et al.
Advanced Functional Materials (2022) Vol. 32, Iss. 17
Closed Access | Times Cited: 71
Machine learning in energy storage materials
Zhonghui Shen, Hanxing Liu, Yang Shen, et al.
Interdisciplinary materials (2022) Vol. 1, Iss. 2, pp. 175-195
Open Access | Times Cited: 66
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
Venkat Pavan Nemani, Luca Biggio, Xun Huan, et al.
Mechanical Systems and Signal Processing (2023) Vol. 205, pp. 110796-110796
Open Access | Times Cited: 66
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: 59
Target‐Driven Design of Deep‐UV Nonlinear Optical Materials via Interpretable Machine Learning
Mengfan Wu, Evgenii Tikhonov, Abudukadi Tudi, et al.
Advanced Materials (2023) Vol. 35, Iss. 23
Closed Access | Times Cited: 49
Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure
Dylan J. Walsh, Weizhong Zou, Ludwig Schneider, et al.
ACS Central Science (2023) Vol. 9, Iss. 3, pp. 330-338
Open Access | Times Cited: 39
Machine Learning Descriptors for Data‐Driven Catalysis Study
Li‐Hui Mou, TianTian Han, Pieter E. S. Smith, et al.
Advanced Science (2023) Vol. 10, Iss. 22
Open Access | Times Cited: 39
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: 37
Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis
Jing Lin, Zhimeng Liu, Yujie Guo, et al.
Nano Today (2023) Vol. 49, pp. 101802-101802
Closed Access | Times Cited: 36
Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization
Liqianyun Xu, Feng Wu, Renjie Chen, et al.
Energy storage materials (2023) Vol. 59, pp. 102785-102785
Closed Access | Times Cited: 36
Chemical Flexibility of Atomically Precise Metal Clusters
Si Li, Nana Li, Xi‐Yan Dong, et al.
Chemical Reviews (2024) Vol. 124, Iss. 11, pp. 7262-7378
Closed Access | Times Cited: 27
Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities
Eslam G. Al-Sakkari, Ahmed Ragab, Hanane Dagdougui, et al.
The Science of The Total Environment (2024) Vol. 917, pp. 170085-170085
Closed Access | Times Cited: 23
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