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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:

Sparse machine learning assisted deep computational insights on the mechanical properties of graphene with intrinsic defects and doping
Kritesh Kumar Gupta, T. Mukhopadhyay, Aditya Roy, et al.
Journal of Physics and Chemistry of Solids (2021) Vol. 155, pp. 110111-110111
Closed Access | Times Cited: 41

Showing 1-25 of 41 citing articles:

Machine learning and deep learning in phononic crystals and metamaterials – A review
Muhammad Gulzari, John F. Kennedy, C.W. Lim
Materials Today Communications (2022) Vol. 33, pp. 104606-104606
Closed Access | Times Cited: 77
Recent advances in machine learning guided mechanical properties prediction and design of two-dimensional materials
Rui Liu, Lin Shu, Jing Wan, et al.
Thin-Walled Structures (2025), pp. 113261-113261
Closed Access | Times Cited: 1
Analysis and evaluation of machine learning applications in materials design and discovery
Mahsa Golmohammadi, Masoud Aryanpour
Materials Today Communications (2023) Vol. 35, pp. 105494-105494
Closed Access | Times Cited: 20
Probing the molecular-level energy absorption mechanism and strategic sequencing of graphene/Al composite laminates under high-velocity ballistic impact of nano-projectiles
Kritesh Kumar Gupta, T. Mukhopadhyay, S. Dey
Applied Surface Science (2023) Vol. 629, pp. 156502-156502
Open Access | Times Cited: 19
On exploiting nonparametric kernel-based probabilistic machine learning over the large compositional space of high entropy alloys for optimal nanoscale ballistics
Kritesh Kumar Gupta, Surajit Das Barman, S. Dey, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Multiscale computational modeling techniques in study and design of 2D materials: recent advances, challenges, and opportunities
Mohsen Asle Zaeem, Siby Thomas, Sepideh Kavousi, et al.
2D Materials (2024) Vol. 11, Iss. 4, pp. 042004-042004
Open Access | Times Cited: 7
Recent Advances of Graphene and Related Materials in Artificial Intelligence
Meirong Huang, Zechen Li, Hongwei Zhu
Advanced Intelligent Systems (2022) Vol. 4, Iss. 10
Open Access | Times Cited: 23
Machine learning mechanical properties of defect-engineered hexagonal boron nitride
Yi Shen, Shuze Zhu
Computational Materials Science (2023) Vol. 220, pp. 112030-112030
Closed Access | Times Cited: 15
Hybrid machine-learning-assisted stochastic nano-indentation behaviour of twisted bilayer graphene
Kritesh Kumar Gupta, Lintu Roy, Sudip Dey
Journal of Physics and Chemistry of Solids (2022) Vol. 167, pp. 110711-110711
Closed Access | Times Cited: 21
Probing the stochastic fracture behavior of twisted bilayer graphene: Efficient ANN based molecular dynamics simulations for complete probabilistic characterization
Kritesh Kumar Gupta, Aditya Roy, T. Mukhopadhyay, et al.
Materials Today Communications (2022) Vol. 32, pp. 103932-103932
Closed Access | Times Cited: 19
Machine learning-based study of hardness in polypropylene/carbon nanotube and low-density polyethylene/carbon nanotube composites
Harshit Sharma, Gaurav Arora, Raj Kumar, et al.
Discover Materials (2025) Vol. 5, Iss. 1
Open Access
Enhancing robustness in machine-learning-accelerated molecular dynamics: A multi-model nonparametric probabilistic approach
Ariana Quek, Niuchang Ouyang, H. H. Lin, et al.
Mechanics of Materials (2025) Vol. 202, pp. 105237-105237
Closed Access
Machine learning with MD
Sumit Sharma, P. Shahbaz
Elsevier eBooks (2025), pp. 581-608
Closed Access
Machine learning and molecular dynamics
Sumit Sharma, P. Shahbaz
Elsevier eBooks (2025), pp. 473-482
Closed Access
Modeling the large deformation behavior of CNTs via variational method
Reza Masoudi Nejad, Massoud Mir, Danial Ghahremani Moghadam, et al.
Diamond and Related Materials (2025), pp. 112426-112426
Closed Access
Data-driven probabilistic performance of Wire EDM: A machine learning based approach
Subhankar Saha, Kritesh Kumar Gupta, Saikat Ranjan Maity, et al.
Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture (2021) Vol. 236, Iss. 6-7, pp. 908-919
Closed Access | Times Cited: 21
‘Magic’ of twisted multi-layered graphene and 2D nano-heterostructures
K Saumya, Susmita Naskar, T. Mukhopadhyay
Nano Futures (2023) Vol. 7, Iss. 3, pp. 032005-032005
Open Access | Times Cited: 9
Machine Learning Enabled Prediction of High Stiffness 2D Materials
Hema Rajesh Nadella, Sankha Mukherjee, Abu Anand, et al.
ACS Materials Letters (2024) Vol. 6, Iss. 2, pp. 729-736
Closed Access | Times Cited: 3
Probing the Stochastic Unconfined Compressive Strength of Lime–RHA Mix Treated Clayey Soil
Gautam, Kritesh Kumar Gupta, Debjit Bhowmik, et al.
Journal of Materials in Civil Engineering (2022) Vol. 35, Iss. 3
Closed Access | Times Cited: 14
Hybrid machine-learning-assisted quantification of the compound internal and external uncertainties of graphene: towards inclusive analysis and design
Kritesh Kumar Gupta, T. Mukhopadhyay, Lintu Roy, et al.
Materials Advances (2021) Vol. 3, Iss. 2, pp. 1160-1181
Open Access | Times Cited: 16
Probing the mechanical and deformation behaviour of CNT-reinforced AlCoCrFeNi high-entropy alloy – a molecular dynamics approach
Subrata Barman, Sudip Dey
Molecular Simulation (2023) Vol. 49, Iss. 18, pp. 1726-1741
Closed Access | Times Cited: 6
Probabilistic investigation of temperature-dependent vibrational behavior of hetero-nanotubes
Aditya Roy, Kritesh Kumar Gupta, Sudip Dey
Applied Nanoscience (2022) Vol. 12, Iss. 7, pp. 2077-2089
Closed Access | Times Cited: 10
Sensitivity Analysis of Random Frequency Responses of Hybrid Multi-functionally Graded Sandwich Shells
Vaishali Vaishali, Subrata Kushari, R. R. Kumar, et al.
Journal of Vibration Engineering & Technologies (2022) Vol. 11, Iss. 3, pp. 845-872
Closed Access | Times Cited: 8
Stochastic Performance of Journal Bearing With Two-Layered Porous Bush—A Machine Learning Approach
Subrata Barman, Kritesh Kumar Gupta, Subrata Kushari, et al.
Journal of Tribology (2023) Vol. 145, Iss. 10
Closed Access | Times Cited: 4
Atomistic simulation assisted error-inclusive Bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals
Avik Mahata, T. Mukhopadhyay, Souvik Chakraborty, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 1
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