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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:

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

Showing 1-25 of 129 citing articles:

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges
Guang Chen, Zhiqiang Shen, Akshay Iyer, et al.
Polymers (2020) Vol. 12, Iss. 1, pp. 163-163
Open Access | Times Cited: 157
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: 149
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: 108
A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?
Yaoguang Zhai, Alessandro Caruso, Sigbjørn Løland Bore, et al.
The Journal of Chemical Physics (2023) Vol. 158, Iss. 8
Open Access | Times Cited: 61
A novel graphene-like titanium carbide MXene/Au–Ag nanoshuttles bifunctional nanosensor for electrochemical and SERS intelligent analysis of ultra-trace carbendazim coupled with machine learning
Xiaoyu Zhu, Peng Liu, Ting Xue, et al.
Ceramics International (2020) Vol. 47, Iss. 1, pp. 173-184
Closed Access | Times Cited: 110
Combustion in the future: The importance of chemistry
Katharina Kohse‐Höinghaus
Proceedings of the Combustion Institute (2020) Vol. 38, Iss. 1, pp. 1-56
Open Access | Times Cited: 103
Molecule Attention Transformer
Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 102
Learning to Make Chemical Predictions: The Interplay of Feature Representation, Data, and Machine Learning Methods
Mojtaba Haghighatlari, Jie Li, Farnaz Heidar‐Zadeh, et al.
Chem (2020) Vol. 6, Iss. 7, pp. 1527-1542
Open Access | Times Cited: 94
Artificial intelligence: machine learning for chemical sciences
A. Karthikeyan, U. Deva Priyakumar
Journal of Chemical Sciences (2021) Vol. 134, Iss. 1
Open Access | Times Cited: 66
Transfer learning for materials informatics using crystal graph convolutional neural network
Joohwi Lee, Ryoji Asahi
Computational Materials Science (2021) Vol. 190, pp. 110314-110314
Open Access | Times Cited: 65
Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities
Nadia Terranova, Karthik Venkatakrishnan, Lisa J. Benincosa
The AAPS Journal (2021) Vol. 23, Iss. 4
Open Access | Times Cited: 57
A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
Benedikt Winter, Clemens Winter, Johannes Schilling, et al.
Digital Discovery (2022) Vol. 1, Iss. 6, pp. 859-869
Open Access | Times Cited: 55
Augmented Reality, a Review of a Way to Represent and Manipulate 3D Chemical Structures
Alba Fombona‐Pascual, Javier Fombona Cadavieco, R. Vicente
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 8, pp. 1863-1872
Open Access | Times Cited: 44
Development of machine learning algorithms for predicting internal corrosion of crude oil and natural gas pipelines
Jian Fang, Xiao Cheng, Huilong Gai, et al.
Computers & Chemical Engineering (2023) Vol. 177, pp. 108358-108358
Closed Access | Times Cited: 32
Many-body interactions and deep neural network potentials for water
Yaoguang Zhai, Richa Rashmi, Etienne Palos, et al.
The Journal of Chemical Physics (2024) Vol. 160, Iss. 14
Open Access | Times Cited: 11
Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks
Alex Casey, Steven F. Son, Ilias Bilionis, et al.
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 10, pp. 4457-4473
Closed Access | Times Cited: 69
ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data
Mojtaba Haghighatlari, Gaurav Vishwakarma, Doaa Altarawy, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2020) Vol. 10, Iss. 4
Open Access | Times Cited: 56
Progress in Modeling of Biomass Fast Pyrolysis: A Review
Pavlo Kostetskyy, Linda J. Broadbelt
Energy & Fuels (2020) Vol. 34, Iss. 12, pp. 15195-15216
Closed Access | Times Cited: 56
Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and Best Practices for Machine Learning in Chemistry
Gaurav Vishwakarma, Aditya Sonpal, Johannes Hachmann
Trends in Chemistry (2021) Vol. 3, Iss. 2, pp. 146-156
Open Access | Times Cited: 55
Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field
Tamar Schlick, Stephanie Portillo‐Ledesma, Christopher G. Myers, et al.
Annual Review of Biophysics (2021) Vol. 50, Iss. 1, pp. 267-301
Open Access | Times Cited: 50
Green solvent screening using modeling and simulation
María González‐Miquel, Ismael Díaz
Current Opinion in Green and Sustainable Chemistry (2021) Vol. 29, pp. 100469-100469
Closed Access | Times Cited: 48
Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design
Thanh Danh Nguyen, Lei Tao, Ying Li
Frontiers in Chemistry (2022) Vol. 9
Open Access | Times Cited: 38
VR in chemistry, a review of scientific research on advanced atomic/molecular visualization
Alba Fombona‐Pascual, Javier Fombona Cadavieco, Esteban Vázquez Cano
Chemistry Education Research and Practice (2022) Vol. 23, Iss. 2, pp. 300-312
Closed Access | Times Cited: 30
Data-Driven Many-Body Potential Energy Functions for Generic Molecules: Linear Alkanes as a Proof-of-Concept Application
Ethan F. Bull-Vulpe, Marc Riera, Sigbjørn Løland Bore, et al.
Journal of Chemical Theory and Computation (2022) Vol. 19, Iss. 14, pp. 4494-4509
Closed Access | Times Cited: 30
Machine Learning of Coupled Cluster (T)-Energy Corrections via Delta (Δ)-Learning
Marcel Ruth, Dennis Gerbig, Peter R. Schreiner
Journal of Chemical Theory and Computation (2022) Vol. 18, Iss. 8, pp. 4846-4855
Closed Access | Times Cited: 29
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