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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:

A high throughput molecular screening for organic electronics via machine learning: present status and perspective
Akinori Saeki, Kakaraparthi Kranthiraja
Japanese Journal of Applied Physics (2019) Vol. 59, Iss. SD, pp. SD0801-SD0801
Open Access | Times Cited: 66

Showing 1-25 of 66 citing articles:

Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry
Rizvi Syed Aal E Ali, Jiaolong Meng, Muhammad Ehtisham Ibraheem Khan, et al.
Artificial Intelligence Chemistry (2024) Vol. 2, Iss. 1, pp. 100049-100049
Open Access | Times Cited: 28
Active discovery of organic semiconductors
Christian Künkel, Johannes T. Margraf, Ke Chen, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 101
High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications
Qingsong Wang, Leonardo Velasco, Ben Breitung, et al.
Advanced Energy Materials (2021) Vol. 11, Iss. 47
Open Access | Times Cited: 78
Experiment‐Oriented Machine Learning of Polymer:Non‐Fullerene Organic Solar Cells
Kakaraparthi Kranthiraja, Akinori Saeki
Advanced Functional Materials (2021) Vol. 31, Iss. 23
Closed Access | Times Cited: 73
Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks
Yuta Miyake, Akinori Saeki
The Journal of Physical Chemistry Letters (2021) Vol. 12, Iss. 51, pp. 12391-12401
Closed Access | Times Cited: 73
Organic crystal structure prediction and its application to materials design
Qiang Zhu, Shinnosuke Hattori
Journal of materials research/Pratt's guide to venture capital sources (2022) Vol. 38, Iss. 1, pp. 19-36
Open Access | Times Cited: 53
Data-driven design of novel halide perovskite alloys
Arun Mannodi‐Kanakkithodi, Maria K. Y. Chan
Energy & Environmental Science (2022) Vol. 15, Iss. 5, pp. 1930-1949
Open Access | Times Cited: 47
Artificial Intelligence for Conjugated Polymers
Qiaomu Yang, Aikaterini Vriza, Cesar A. Castro Rubio, et al.
Chemistry of Materials (2024) Vol. 36, Iss. 6, pp. 2602-2622
Closed Access | Times Cited: 15
Advances in materials informatics: a review
Dawn Sivan, K. Satheesh Kumar, Aziman Abdullah, et al.
Journal of Materials Science (2024) Vol. 59, Iss. 7, pp. 2602-2643
Closed Access | Times Cited: 12
Introduction to Predicting Properties of Organic Materials
Didier Mathieu
Challenges and advances in computational chemistry and physics (2025), pp. 27-63
Closed Access | Times Cited: 1
Evaluation-oriented exploration of photo energy conversion systems: from fundamental optoelectronics and material screening to the combination with data science
Akinori Saeki
Polymer Journal (2020) Vol. 52, Iss. 12, pp. 1307-1321
Open Access | Times Cited: 62
High-throughput virtual screening for organic electronics: a comparative study of alternative strategies
Ömer H. Omar, Marcos del Cueto, Tahereh Nematiaram, et al.
Journal of Materials Chemistry C (2021) Vol. 9, Iss. 39, pp. 13557-13583
Open Access | Times Cited: 44
Efficient screening framework for organic solar cells with deep learning and ensemble learning
Hongshuai Wang, Jie Feng, Zhihao Dong, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 21
Phase–Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries
Leonardo Velasco, Juan Sebastián Castillo, Mohana V. Kante, et al.
Advanced Materials (2021) Vol. 33, Iss. 43
Open Access | Times Cited: 41
OCELOT: An infrastructure for data-driven research to discover and design crystalline organic semiconductors
Qianxiang Ai, Vinayak Bhat, Sean M. Ryno, et al.
The Journal of Chemical Physics (2021) Vol. 154, Iss. 17
Open Access | Times Cited: 38
Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics
Zhi‐Wen Zhao, Yun Geng, Alessandro Troisi, et al.
Advanced Intelligent Systems (2022) Vol. 4, Iss. 6
Open Access | Times Cited: 23
Machine learning in energy chemistry: introduction, challenges and perspectives
Yuzhi Xu, Jiankai Ge, Cheng‐Wei Ju
Energy Advances (2023) Vol. 2, Iss. 7, pp. 896-921
Open Access | Times Cited: 15
Computational Identification of Novel Families of Nonfullerene Acceptors by Modification of Known Compounds
Zhi‐Wen Zhao, Ömer H. Omar, Daniele Padula, et al.
The Journal of Physical Chemistry Letters (2021) Vol. 12, Iss. 20, pp. 5009-5015
Open Access | Times Cited: 29
Machine Learning-Assisted Polymer Design for Improving the Performance of Non-Fullerene Organic Solar Cells
Kakaraparthi Kranthiraja, Akinori Saeki
ACS Applied Materials & Interfaces (2022) Vol. 14, Iss. 25, pp. 28936-28944
Closed Access | Times Cited: 21
Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance
Marcos del Cueto, Charles Rawski-Furman, Juan Aragó, et al.
The Journal of Physical Chemistry C (2022) Vol. 126, Iss. 31, pp. 13053-13061
Open Access | Times Cited: 20
Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices
Martin Seifrid, Stanley Lo, Dylan G. Choi, et al.
Journal of Materials Chemistry A (2024) Vol. 12, Iss. 24, pp. 14540-14558
Open Access | Times Cited: 4
Machine-Learned Charge Transfer Integrals for Multiscale Simulations in Organic Thin Films
Michael Rinderle, Waldemar Kaiser, Alessandro Mattoni, et al.
The Journal of Physical Chemistry C (2020) Vol. 124, Iss. 32, pp. 17733-17743
Closed Access | Times Cited: 32
Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data
Yuta Miyake, Kakaraparthi Kranthiraja, Fumitaka Ishiwari, et al.
Chemistry of Materials (2022) Vol. 34, Iss. 15, pp. 6912-6920
Closed Access | Times Cited: 17
Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning
Jie Feng, Hongshuai Wang, Yujin Ji, et al.
Nano Select (2021) Vol. 2, Iss. 9, pp. 1629-1641
Open Access | Times Cited: 22
Impact of substituents on the performance of small-molecule semiconductors in organic photovoltaic devices via regulating morphology
Mitsuharu Suzuki, K Suzuki, Taehyun Won, et al.
Journal of Materials Chemistry C (2022) Vol. 10, Iss. 4, pp. 1162-1195
Open Access | Times Cited: 15
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