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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:

Chemical data intelligence for sustainable chemistry
Jana M. Weber, Zhen Guo, Chonghuan Zhang, et al.
Chemical Society Reviews (2021) Vol. 50, Iss. 21, pp. 12013-12036
Open Access | Times Cited: 41

Showing 1-25 of 41 citing articles:

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors
Li‐Tao Zhu, Xizhong Chen, Bo Ouyang, et al.
Industrial & Engineering Chemistry Research (2022) Vol. 61, Iss. 28, pp. 9901-9949
Open Access | Times Cited: 133
Analytical Tools Integrated in Continuous-Flow Reactors: Which One for What?
Mireia Rodriguez‐Zubiri, François‐Xavier Felpin
Organic Process Research & Development (2022) Vol. 26, Iss. 6, pp. 1766-1793
Open Access | Times Cited: 38
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
Gabriel Vogel, Lukas Schulze Balhorn, Artur M. Schweidtmann
Computers & Chemical Engineering (2023) Vol. 171, pp. 108162-108162
Open Access | Times Cited: 35
Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant
Kevin Maik Jablonka, Charithea Charalambous, Eva Sánchez Fernández, et al.
Science Advances (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 20
Catalytic macrocyclization of unactivated C(sp3)-H bond in natural product synthesis
Zhuo Wang
Tetrahedron Green Chem (2025), pp. 100064-100064
Open Access
Digitization of chemical process flow diagrams using deep convolutional neural networks
Maximilian F. Theisen, Kenji Nishizaki Flores, Lukas Schulze Balhorn, et al.
Digital Chemical Engineering (2022) Vol. 6, pp. 100072-100072
Open Access | Times Cited: 25
Transforming research laboratories with connected digital twins
Simon D. Rihm, Jiaru Bai, Aleksandar Kondinski, et al.
Deleted Journal (2024) Vol. 1, Iss. 1, pp. 100004-100004
Open Access | Times Cited: 4
Biosynthesis of biomolecules from saffron as an industrial crop and their regulation, with emphasis on the chemistry, extraction methods, identification techniques, and potential applications in human health and food: A critical comprehensive review
Vishal Gupta, Gayatri Jamwal, G. Rai, et al.
Biocatalysis and Agricultural Biotechnology (2024) Vol. 59, pp. 103260-103260
Closed Access | Times Cited: 3
Completing and Balancing Database Excerpted Chemical Reactions with a Hybrid Mechanistic-Machine Learning Approach
Chonghuan Zhang, Adarsh Arun, Alexei A. Lapkin
ACS Omega (2024) Vol. 9, Iss. 16, pp. 18385-18399
Open Access | Times Cited: 3
Retrosynthesis from transforms to predictive sustainable chemistry and nanotechnology: a brief tutorial review
Alicja Mikołajczyk, Uladzislau Zhdan, Sylvain Antoniotti, et al.
Green Chemistry (2023) Vol. 25, Iss. 8, pp. 2971-2991
Closed Access | Times Cited: 8
Outlook: How I Learned to Love Machine Learning (A Personal Perspective on Machine Learning in Process Systems Engineering)
Ví­ctor M. Zavala
Industrial & Engineering Chemistry Research (2023) Vol. 62, Iss. 23, pp. 8995-9005
Open Access | Times Cited: 8
Reinforcement learning optimization of reaction routes on the basis of large, hybrid organic chemistry–synthetic biological, reaction network data
Chonghuan Zhang, Alexei A. Lapkin
Reaction Chemistry & Engineering (2023) Vol. 8, Iss. 10, pp. 2491-2504
Open Access | Times Cited: 8
Artificial intelligence and machine learning at various stages and scales of process systems engineering
Karthik K. Srinivasan, Anjana Puliyanda, Devavrat Thosar, et al.
The Canadian Journal of Chemical Engineering (2024) Vol. 103, Iss. 3, pp. 1004-1035
Open Access | Times Cited: 2
Composition-driven archetype dynamics in polyoxovanadates
Aleksandar Kondinski, Maren Rasmussen, Sebastian Mangelsen, et al.
Chemical Science (2022) Vol. 13, Iss. 21, pp. 6397-6412
Open Access | Times Cited: 12
The Role of AI in Driving the Sustainability of the Chemical Industry
Alessandra Toniato, Oliver Schilter, Teodoro Laino
CHIMIA International Journal for Chemistry (2023) Vol. 77, Iss. 3, pp. 144-144
Open Access | Times Cited: 6
Knowledge Engineering in Chemistry: From Expert Systems to Agents of Creation
Aleksandar Kondinski, Jiaru Bai, Sebastian Mosbach, et al.
Accounts of Chemical Research (2022) Vol. 56, Iss. 2, pp. 128-139
Open Access | Times Cited: 11
Completing and balancing database excerpted chemical reactions with a hybrid mechanistic - machine learning approach
Chonghuan Zhang, Adarsh Arun, Alexei A. Lapkin
(2023)
Open Access | Times Cited: 5
The need to integrate mass- and energy-based metrics with life cycle impacts for sustainable chemicals manufacture
Elysia Lucas, Antonio J. Martín, Sharon Mitchell, et al.
Green Chemistry (2024) Vol. 26, Iss. 17, pp. 9300-9309
Open Access | Times Cited: 1
SynAsk: Unleashing the Power of Large Language Models in Organic Synthesis
Chonghuan Zhang, Qianghua Lin, Biwei Zhu, et al.
Chemical Science (2024)
Open Access | Times Cited: 1
SKG4EOSC - Scholarly Knowledge Graphs for EOSC: Establishing a backbone of knowledge graphs for FAIR Scholarly Information in EOSC
Markus Stocker, Tina Heger, Artur M. Schweidtmann, et al.
Research Ideas and Outcomes (2022) Vol. 8
Open Access | Times Cited: 8
Dissociation cross sections and rates in O2 + N collisions: molecular dynamics simulations combined with machine learning
Xia Huang, Kun-Ming Gu, Chang-Min Guo, et al.
Physical Chemistry Chemical Physics (2023) Vol. 25, Iss. 43, pp. 29475-29485
Closed Access | Times Cited: 4
Flowsheet Recognition using Deep Convolutional Neural Networks
Lukas Schulze Balhorn, Qinghe Gao, Dominik Goldstein, et al.
Computer-aided chemical engineering/Computer aided chemical engineering (2022), pp. 1567-1572
Closed Access | Times Cited: 7
Accurate prediction of chemical exergy of technical lignins for exergy-based assessment on sustainable utilization processes
Youwang Huang, Haiyong Wang, Xinghua Zhang, et al.
Energy (2021) Vol. 243, pp. 123041-123041
Closed Access | Times Cited: 9
Boosting autonomous process design and intensification with formalized domain knowledge
J. Raphael Seidenberg, Ahmad A. Khan, Alexei A. Lapkin
Computers & Chemical Engineering (2022) Vol. 169, pp. 108097-108097
Open Access | Times Cited: 5
Automatic data‐driven stoichiometry identification and kinetic modeling framework for homogeneous organic reactions
Yafeng Xing, Yachao Dong, Christos Goergakis, et al.
AIChE Journal (2022) Vol. 68, Iss. 7
Closed Access | Times Cited: 5
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