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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:

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: 118

Showing 26-50 of 118 citing articles:

Machine-learning-assisted discovery of highly efficient high-entropy alloy catalysts for the oxygen reduction reaction
Xuhao Wan, Zhaofu Zhang, Wei Yu, et al.
Patterns (2022) Vol. 3, Iss. 9, pp. 100553-100553
Open Access | Times Cited: 40
Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence
Lucas Foppa, Christopher Sutton, Luca M. Ghiringhelli, et al.
ACS Catalysis (2022) Vol. 12, Iss. 4, pp. 2223-2232
Open Access | Times Cited: 39
MatGPT: A Vane of Materials Informatics from Past, Present, to Future
Zhilong Wang, An Chen, Kehao Tao, et al.
Advanced Materials (2023) Vol. 36, Iss. 6
Closed Access | Times Cited: 31
Rapid Data‐Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion
Carola Lampe, Ioannis Kouroudis, Milan Harth, et al.
Advanced Materials (2023) Vol. 35, Iss. 16
Open Access | Times Cited: 29
Interpretable Catalysis Models Using Machine Learning with Spectroscopic Descriptors
Song Wang, Jun Jiang
ACS Catalysis (2023) Vol. 13, Iss. 11, pp. 7428-7436
Closed Access | Times Cited: 28
Crystal Facet and Electronic Structure Modulation of Perovskite Oxides for Water Oxidation
Ungsoo Kim, Sang-Jin Lee, Donghwan Koo, et al.
ACS Energy Letters (2023) Vol. 8, Iss. 3, pp. 1575-1583
Closed Access | Times Cited: 24
Machine Learning-Driven Discovery of Key Descriptors for CO2Activation over Two-Dimensional Transition Metal Carbides and Nitrides
B. Moses Abraham, Oriol Piqué, Mohd Aamir Khan, et al.
ACS Applied Materials & Interfaces (2023) Vol. 15, Iss. 25, pp. 30117-30126
Open Access | Times Cited: 24
Predicting the work function of 2D MXenes using machine-learning methods
Pranav Roy, Lavie Rekhi, See Wee Koh, et al.
Journal of Physics Energy (2023) Vol. 5, Iss. 3, pp. 034005-034005
Open Access | Times Cited: 23
Recent computational insights into hydrogen storage by MXene-based materials and shedding light on the storage mechanism
Türkan Kopaç
Journal of Energy Storage (2024) Vol. 97, pp. 112807-112807
Closed Access | Times Cited: 12
Catalysis in the digital age: Unlocking the power of data with machine learning
B. Moses Abraham, M. V. Jyothirmai, Priyanka Sinha, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2024) Vol. 14, Iss. 5
Open Access | Times Cited: 11
Transfer learning guided discovery of efficient perovskite oxide for alkaline water oxidation
Chang Jiang, Hongyuan He, Hongquan Guo, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 10
Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion
Tianyi Wang, Qilong Wu, Yun Han, et al.
Applied Physics Reviews (2025) Vol. 12, Iss. 1
Open Access | Times Cited: 1
Oxygen-deficient perovskites for oxygen evolution reaction in alkaline media: a review
Ahmed Badreldin, Aya E. Abusrafa, Ahmed Abdel‐Wahab
Emergent Materials (2020) Vol. 3, Iss. 5, pp. 567-590
Open Access | Times Cited: 62
Applied Machine Learning for Developing Next‐Generation Functional Materials
Filip Dinic, Kamalpreet Singh, Tony Dong, et al.
Advanced Functional Materials (2021) Vol. 31, Iss. 51
Closed Access | Times Cited: 42
Deep Learning-Assisted Investigation of Electric Field–Dipole Effects on Catalytic Ammonia Synthesis
Mingyu Wan, Han Yue, Jaime Notarangelo, et al.
JACS Au (2022) Vol. 2, Iss. 6, pp. 1338-1349
Open Access | Times Cited: 36
Machine learning utilized for the development of proton exchange membrane electrolyzers
Rui Ding, Yawen Chen, Zhiyan Rui, et al.
Journal of Power Sources (2022) Vol. 556, pp. 232389-232389
Closed Access | Times Cited: 29
Surface‐Functionalized Nanoparticles as Catalysts for Artificial Photosynthesis
Gerard Martí, Laura Mallón, Nuria Romero, et al.
Advanced Energy Materials (2023) Vol. 13, Iss. 21
Open Access | Times Cited: 21
Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective
Ahmed Badreldin, O. Bouhali, Ahmed Abdel‐Wahab
Advanced Functional Materials (2023) Vol. 34, Iss. 12
Closed Access | Times Cited: 21
Urea Electrosynthesis Accelerated by Theoretical Simulations
Junxian Liu, Xiangyu Guo, Thomas Frauenheim, et al.
Advanced Functional Materials (2023) Vol. 34, Iss. 14
Open Access | Times Cited: 21
Interpretable Machine Learning for Catalytic Materials Design toward Sustainability
Hongliang Xin, Tianyou Mou, Hemanth Somarajan Pillai, et al.
Accounts of Materials Research (2023) Vol. 5, Iss. 1, pp. 22-34
Open Access | Times Cited: 19
The design and optimization of heterogeneous catalysts using computational methods
Shambhawi Shambhawi, Ojus Mohan, Tej S. Choksi, et al.
Catalysis Science & Technology (2023) Vol. 14, Iss. 3, pp. 515-532
Open Access | Times Cited: 19
Anion Exchange Membrane Water Electrolyzers: An Overview
Yuuki Sugawara, Sasidharan Sankar, Shoji Miyanishi, et al.
JOURNAL OF CHEMICAL ENGINEERING OF JAPAN (2023) Vol. 56, Iss. 1
Open Access | Times Cited: 18
Interpretable machine learning-assisted screening of perovskite oxides
Jie Zhao, Xiaoyan Wang, Haobo Li, et al.
RSC Advances (2024) Vol. 14, Iss. 6, pp. 3909-3922
Open Access | Times Cited: 8
Screening of transition metal dual-atom catalysts for hydrogen evolution reaction based on high-throughput calculation and searching surrogate prediction model using simple features
Jiu-Ning Wang, Wei Xu, Jun He, et al.
Applied Surface Science (2024) Vol. 659, pp. 159942-159942
Closed Access | Times Cited: 8
Catalyst design with machine learning
Hongliang Xin
Nature Energy (2022) Vol. 7, Iss. 9, pp. 790-791
Closed Access | Times Cited: 26

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