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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:

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

Showing 19 citing articles:

Programmable multi-physical mechanics of mechanical metamaterials
P K Sinha, T. Mukhopadhyay
Materials Science and Engineering R Reports (2023) Vol. 155, pp. 100745-100745
Open Access | Times Cited: 92
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
Graphene/epoxy nanocomposites for improved fracture toughness: A focused review on toughening mechanism
Muhammad Yasir Khalid, Abdallah Kamal, Adam Otabil, et al.
Chemical Engineering Journal Advances (2023) Vol. 16, pp. 100537-100537
Open Access | Times Cited: 62
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
Explainable machine learning assisted molecular-level insights for enhanced specific stiffness exploiting the large compositional space of AlCoCrFeNi high entropy alloys
Kritesh Kumar Gupta, Subrata Barman, S. Dey, et al.
Machine Learning Science and Technology (2024) Vol. 5, Iss. 2, pp. 025082-025082
Open Access | Times Cited: 7
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
Enhancing mechanical performance of Al0.3CoCrFeNi HEA films through graphene coating: insights from nanoindentation and dislocation mechanism analysis
Subrata Barman, Kritesh Kumar Gupta, Sudip Dey
Modelling and Simulation in Materials Science and Engineering (2024) Vol. 32, Iss. 3, pp. 035012-035012
Closed Access | Times Cited: 6
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
‘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
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
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
Influence of Alloying Elements on Mechanical Deformation of AlCoCrFeNi High-Entropy Alloy
Subrata Barman, Kritesh Kumar Gupta, Sudip Dey
Lecture notes in mechanical engineering (2024), pp. 295-303
Closed Access | Times Cited: 1
Prediction of the critical temperature of superconducting materials using image regression and ensemble deep learning
AmirMasoud Taheri, Hossein Ebrahimnezhad, Mohammad Hossein Sedaaghi
Materials Today Communications (2022) Vol. 33, pp. 104743-104743
Closed Access | Times Cited: 5
Neural network accelerated process design of polycrystalline microstructures
Junrong Lin, Mahmudul Hasan, Pınar Acar, et al.
Materials Today Communications (2023) Vol. 36, pp. 106884-106884
Open Access | Times Cited: 2
Molecular dynamics study on the mechanical behavior of vertically aligned γ-graphdiyne-graphene heterostructures under tension
Zhaozhao Wang, Hengyun Zhang, Xun Sun, et al.
Materials Today Communications (2023) Vol. 37, pp. 107465-107465
Closed Access | Times Cited: 1
Vibration Analysis of Cracked Cantilever Beam Using Response Surface Methodology
DevDatt Pathak, Subrata Kushari, Saikat Ranjan Maity, et al.
Journal of Vibration Engineering & Technologies (2022) Vol. 11, Iss. 5, pp. 2429-2452
Closed Access | Times Cited: 1
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