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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:

Interpretable machine learning for knowledge generation in heterogeneous catalysis
Jacques A. Esterhuizen, Bryan R. Goldsmith, Suljo Linic
Nature Catalysis (2022) Vol. 5, Iss. 3, pp. 175-184
Closed Access | Times Cited: 243

Showing 1-25 of 243 citing articles:

Bridging the complexity gap in computational heterogeneous catalysis with machine learning
Tianyou Mou, Hemanth Somarajan Pillai, Siwen Wang, et al.
Nature Catalysis (2023) Vol. 6, Iss. 2, pp. 122-136
Closed Access | Times Cited: 133
A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation
Manu Suvarna, Thaylan Pinheiro Araújo, Javier Pérez‐Ramírez
Applied Catalysis B Environment and Energy (2022) Vol. 315, pp. 121530-121530
Open Access | Times Cited: 92
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
Advances in heterogeneous single-cluster catalysis
Xinzhe Li, Sharon Mitchell, Yiyun Fang, et al.
Nature Reviews Chemistry (2023) Vol. 7, Iss. 11, pp. 754-767
Closed Access | Times Cited: 87
Deep insights into the viscosity of deep eutectic solvents by an XGBoost-based model plus SHapley Additive exPlanation
Dingyi Shi, Fengyi Zhou, Wenbo Mu, et al.
Physical Chemistry Chemical Physics (2022) Vol. 24, Iss. 42, pp. 26029-26036
Closed Access | Times Cited: 76
Machine Learning: A New Paradigm in Computational Electrocatalysis
Xu Zhang, Yun Tian, Letian Chen, et al.
The Journal of Physical Chemistry Letters (2022) Vol. 13, Iss. 34, pp. 7920-7930
Closed Access | Times Cited: 72
Advancement of modification engineering in lean methane combustion catalysts based on defect chemistry
Ruishan Qiu, Wei Wang, Zhe Wang, et al.
Catalysis Science & Technology (2023) Vol. 13, Iss. 8, pp. 2566-2584
Closed Access | Times Cited: 53
High‐Throughput Screening of Electrocatalysts for Nitrogen Reduction Reactions Accelerated by Interpretable Intrinsic Descriptor
Xiaoyun Lin, Yongtao Wang, Xin Chang, et al.
Angewandte Chemie International Edition (2023) Vol. 62, Iss. 19
Closed Access | Times Cited: 50
Atomic Aerogel Materials (or Single‐Atom Aerogels): An Interesting New Paradigm in Materials Science and Catalysis Science
Zesheng Li, Bolin Li, Changlin Yu
Advanced Materials (2023) Vol. 35, Iss. 24
Closed Access | Times Cited: 49
Computational chemistry for water-splitting electrocatalysis
Licheng Miao, Wenqi Jia, Xuejie Cao, et al.
Chemical Society Reviews (2024) Vol. 53, Iss. 6, pp. 2771-2807
Closed Access | Times Cited: 48
Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks
Sergio Pablo‐García, Santiago Morandi, Rodrigo A. Vargas–Hernández, et al.
Nature Computational Science (2023) Vol. 3, Iss. 5, pp. 433-442
Open Access | Times Cited: 46
The Enigma of Methanol Synthesis by Cu/ZnO/Al2O3-Based Catalysts
Arik Beck, Mark A. Newton, Leon G. A. van de Water, et al.
Chemical Reviews (2024) Vol. 124, Iss. 8, pp. 4543-4678
Closed Access | Times Cited: 37
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
Materials consideration for the design, fabrication and operation of microscale robots
Chuanrui Chen, Shichao Ding, Joseph Wang
Nature Reviews Materials (2024) Vol. 9, Iss. 3, pp. 159-172
Closed Access | Times Cited: 31
Atomic Design of Alkyne Semihydrogenation Catalysts via Active Learning
Xiaohu Ge, Jun Yin, Zhouhong Ren, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 7, pp. 4993-5004
Closed Access | Times Cited: 23
Embracing data science in catalysis research
Manu Suvarna, Javier Pérez‐Ramírez
Nature Catalysis (2024) Vol. 7, Iss. 6, pp. 624-635
Closed Access | Times Cited: 23
Recent developments and current trends on catalytic dry reforming of Methane: Hydrogen Production, thermodynamics analysis, techno feasibility, and machine learning
Mohammed Mosaad Awad, Esraa Kotob, Omer Ahmed Taialla, et al.
Energy Conversion and Management (2024) Vol. 304, pp. 118252-118252
Closed Access | Times Cited: 22
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design
Jorge Benavides-Hernández, Franck Dumeignil
ACS Catalysis (2024) Vol. 14, Iss. 15, pp. 11749-11779
Closed Access | Times Cited: 21
Transcending scales in catalysis for sustainable development
Sharon Mitchell, Antonio J. Martín, Javier Pérez‐Ramírez
Nature Chemical Engineering (2024) Vol. 1, Iss. 1, pp. 13-15
Open Access | Times Cited: 20
Nature of metal-support interaction for metal catalysts on oxide supports
Tairan Wang, Jianyu Hu, Runhai Ouyang, et al.
Science (2024) Vol. 386, Iss. 6724, pp. 915-920
Closed Access | Times Cited: 18
Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data
Ray Miyazaki, Kendra S. Belthle, Harun Tüysüz, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 8, pp. 5433-5444
Open Access | Times Cited: 16
Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis
Toshiaki Taniike, Aya Fujiwara, Sunao Nakanowatari, et al.
Communications Chemistry (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 15
Material Engineering Strategies for Efficient Hydrogen Evolution Reaction Catalysts
Yue Luo, Yulong Zhang, Jiayi Zhu, et al.
Small Methods (2024)
Open Access | Times Cited: 15
Machine learning-assisted dual-atom sites design with interpretable descriptors unifying electrocatalytic reactions
Xiaoyun Lin, Xiaowei Du, Shican Wu, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 15
Electronic descriptors for designing high-entropy alloy electrocatalysts by leveraging local chemical environments
Guolin Cao, Sha Yang, Ji‐Chang Ren, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access | Times Cited: 2
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