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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:

Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations
Zheng Li, Noushin Omidvar, Wei Shan Chin, et al.
The Journal of Physical Chemistry A (2018) Vol. 122, Iss. 18, pp. 4571-4578
Closed Access | Times Cited: 57

Showing 1-25 of 57 citing articles:

Machine learning for renewable energy materials
Geun Ho Gu, Juhwan Noh, I. P. Kim, et al.
Journal of Materials Chemistry A (2019) Vol. 7, Iss. 29, pp. 17096-17117
Closed Access | Times Cited: 265
Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning
Seoin Back, Kevin Tran, Zachary W. Ulissi
ACS Catalysis (2019) Vol. 9, Iss. 9, pp. 7651-7659
Closed Access | Times Cited: 153
Data‐Driven Materials Innovation and Applications
Zhuo Wang, Zhehao Sun, Hang Yin, et al.
Advanced Materials (2022) Vol. 34, Iss. 36
Closed Access | Times Cited: 106
Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
Aditya Nandy, Chenru Duan, Jon Paul Janet, et al.
Industrial & Engineering Chemistry Research (2018) Vol. 57, Iss. 42, pp. 13973-13986
Open Access | Times Cited: 143
Advances of machine learning in molecular modeling and simulation
Mojtaba Haghighatlari, Johannes Hachmann
Current Opinion in Chemical Engineering (2019) Vol. 23, pp. 51-57
Open Access | Times Cited: 129
An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts
Zheng Li, Luke E. K. Achenie, Hongliang Xin
ACS Catalysis (2020) Vol. 10, Iss. 7, pp. 4377-4384
Closed Access | Times Cited: 117
A review of mathematical representations of biomolecular data
Duc Duy Nguyen, Zixuan Cang, Guo‐Wei Wei
Physical Chemistry Chemical Physics (2020) Vol. 22, Iss. 8, pp. 4343-4367
Open Access | Times Cited: 80
Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation
Aditya Nandy, Jiazhou Zhu, Jon Paul Janet, et al.
ACS Catalysis (2019) Vol. 9, Iss. 9, pp. 8243-8255
Open Access | Times Cited: 79
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
New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts
Aditya Nandy, Chenru Duan, Conrad Goffinet, et al.
JACS Au (2022) Vol. 2, Iss. 5, pp. 1200-1213
Open Access | Times Cited: 53
Machine Learning (ML)‐Assisted Design and Fabrication for Solar Cells
Fan Li, Xiaoqi Peng, Wang Zuo, et al.
Energy & environment materials (2019) Vol. 2, Iss. 4, pp. 280-291
Open Access | Times Cited: 62
Machine Learning for Predicting Electron Transfer Coupling
Chun-I Wang, Mac Kevin E. Braza, Gil C. Claudio, et al.
The Journal of Physical Chemistry A (2019) Vol. 123, Iss. 36, pp. 7792-7802
Closed Access | Times Cited: 57
Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction
Jiawei Zhang, P. Hu, Haifeng Wang
The Journal of Physical Chemistry C (2020) Vol. 124, Iss. 19, pp. 10483-10494
Closed Access | Times Cited: 54
Catalyze Materials Science with Machine Learning
Jaehyun Kim, Donghoon Kang, Sang‐Bum Kim, et al.
ACS Materials Letters (2021) Vol. 3, Iss. 8, pp. 1151-1171
Closed Access | Times Cited: 49
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
Chenru Duan, Fang Liu, Aditya Nandy, et al.
The Journal of Physical Chemistry Letters (2021) Vol. 12, Iss. 19, pp. 4628-4637
Open Access | Times Cited: 44
Machine learning approach for accurate backmapping of coarse-grained models to all-atom models
Yaxin An, Sanket A. Deshmukh
Chemical Communications (2020) Vol. 56, Iss. 65, pp. 9312-9315
Closed Access | Times Cited: 49
Artificial neural networks for predicting charge transfer coupling
Chun-I Wang, Ignasius Joanito, Chang-Feng Lan, et al.
The Journal of Chemical Physics (2020) Vol. 153, Iss. 21
Open Access | Times Cited: 48
Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments
Michael S. Chen, Tim J. Zuehlsdorff, Tobias Morawietz, et al.
The Journal of Physical Chemistry Letters (2020) Vol. 11, Iss. 18, pp. 7559-7568
Open Access | Times Cited: 47
Machine learning the computational cost of quantum chemistry
Stefan Heinen, Max Schwilk, Guido Falk von Rudorff, et al.
Machine Learning Science and Technology (2020) Vol. 1, Iss. 2, pp. 025002-025002
Open Access | Times Cited: 39
Predicting aggregation energy for single atom bimetallic catalysts on clean and O* adsorbed surfaces through machine learning models
Zhuole Lu, Shwetank Yadav, Chandra Veer Singh
Catalysis Science & Technology (2019) Vol. 10, Iss. 1, pp. 86-98
Closed Access | Times Cited: 40
Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels
Marco Eckhoff, K. Nikolas Lausch, Peter E. Blöchl, et al.
The Journal of Chemical Physics (2020) Vol. 153, Iss. 16
Open Access | Times Cited: 33
Key Factors Governing the External Quantum Efficiency of Thermally Activated Delayed Fluorescence Organic Light-Emitting Devices: Evidence from Machine Learning
Haochen Shi, Wenzhu Jing, Wu Liu, et al.
ACS Omega (2022) Vol. 7, Iss. 9, pp. 7893-7900
Open Access | Times Cited: 21
Transfer learning across different chemical domains: virtual screening of organic materials with deep learning models pretrained on small molecule and chemical reaction data
Chengwei Zhang, Yushuang Zhai, Ziyang Gong, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 4
MAG-SOLex Molecular Representation: A Methodology for Handling Complex Molecules in Algorithms
Diego Telles Fernandes, Karina Klock da Costa, Helton Siqueira Maciel, et al.
ACS Omega (2025) Vol. 10, Iss. 6, pp. 5645-5658
Open Access
Enumeration of de novo inorganic complexes for chemical discovery and machine learning
Stefan Gugler, Jon Paul Janet, Heather J. Kulik
Molecular Systems Design & Engineering (2019) Vol. 5, Iss. 1, pp. 139-152
Open Access | Times Cited: 35
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