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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:

Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials
Bohayra Mortazavi, Ivan S. Novikov, Evgeny V. Podryabinkin, et al.
Applied Materials Today (2020) Vol. 20, pp. 100685-100685
Open Access | Times Cited: 175

Showing 1-25 of 175 citing articles:

Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles
Bohayra Mortazavi, Brahmanandam Javvaji, Fazel Shojaei, et al.
Nano Energy (2020) Vol. 82, pp. 105716-105716
Open Access | Times Cited: 471
The MLIP package: moment tensor potentials with MPI and active learning
Ivan S. Novikov, Konstantin Gubaev, Evgeny V. Podryabinkin, et al.
Machine Learning Science and Technology (2020) Vol. 2, Iss. 2, pp. 025002-025002
Open Access | Times Cited: 429
Deep learning for topology optimization of 2D metamaterials
Hunter T. Kollmann, Diab W. Abueidda, Seid Korić, et al.
Materials & Design (2020) Vol. 196, pp. 109098-109098
Open Access | Times Cited: 274
First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials
Bohayra Mortazavi, Mohammad Silani, Evgeny V. Podryabinkin, et al.
Advanced Materials (2021) Vol. 33, Iss. 35
Open Access | Times Cited: 264
Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution
Bohayra Mortazavi, Evgeny V. Podryabinkin, Ivan S. Novikov, et al.
Computer Physics Communications (2020) Vol. 258, pp. 107583-107583
Open Access | Times Cited: 160
Intelligent on-demand design of phononic metamaterials
Yabin Jin, Liangshu He, Zhihui Wen, et al.
Nanophotonics (2022) Vol. 11, Iss. 3, pp. 439-460
Open Access | Times Cited: 103
Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials
Bohayra Mortazavi, Xiaoying Zhuang, Timon Rabczuk, et al.
Materials Horizons (2023) Vol. 10, Iss. 6, pp. 1956-1968
Open Access | Times Cited: 61
Recent advances in the mechanics of 2D materials
Guorui Wang, Hongyu Hou, Yunfeng Yan, et al.
International Journal of Extreme Manufacturing (2023) Vol. 5, Iss. 3, pp. 032002-032002
Open Access | Times Cited: 46
When Machine Learning Meets 2D Materials: A Review
Bin Lu, Yuze Xia, Yuqian Ren, et al.
Advanced Science (2024) Vol. 11, Iss. 13
Open Access | Times Cited: 46
Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations
Guanjie Wang, Changrui Wang, Xuanguang Zhang, et al.
iScience (2024) Vol. 27, Iss. 5, pp. 109673-109673
Open Access | Times Cited: 22
Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
Bohayra Mortazavi
Advanced Energy Materials (2024)
Open Access | Times Cited: 16
Nanoporous C3N4, C3N5 and C3N6 nanosheets; novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties
Bohayra Mortazavi, Fazel Shojaei, Masoud Shahrokhi, et al.
Carbon (2020) Vol. 167, pp. 40-50
Open Access | Times Cited: 114
Deep dive into machine learning density functional theory for materials science and chemistry
Lenz Fiedler, Karan Shah, Michael Bußmann, et al.
Physical Review Materials (2022) Vol. 6, Iss. 4
Open Access | Times Cited: 65
Ab initio prediction of semiconductivity in a novel two-dimensional Sb2X3 (X= S, Se, Te) monolayers with orthorhombic structure
A. Bafekry, Bohayra Mortazavi, Mehrdad Faraji, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 57
A first-principles and machine-learning investigation on the electronic, photocatalytic, mechanical and heat conduction properties of nanoporous C5N monolayers
Bohayra Mortazavi, Masoud Shahrokhi, Fazel Shojaei, et al.
Nanoscale (2022) Vol. 14, Iss. 11, pp. 4324-4333
Closed Access | Times Cited: 41
From prediction to design: Recent advances in machine learning for the study of 2D materials
Hua He, Yuhua Wang, Yajuan Qi, et al.
Nano Energy (2023) Vol. 118, pp. 108965-108965
Closed Access | Times Cited: 40
Anisotropic mechanical response, high negative thermal expansion, and outstanding dynamical stability of biphenylene monolayer revealed by machine-learning interatomic potentials
Bohayra Mortazavi, Alexander V. Shapeev
FlatChem (2022) Vol. 32, pp. 100347-100347
Closed Access | Times Cited: 39
Structural, electronic, thermal and mechanical properties of C60-based fullerene two-dimensional networks explored by first-principles and machine learning
Bohayra Mortazavi
Carbon (2023) Vol. 213, pp. 118293-118293
Open Access | Times Cited: 34
Accelerating the prediction of stable materials with machine learning
Sean D. Griesemer, Yi Xia, Chris Wolverton
Nature Computational Science (2023) Vol. 3, Iss. 11, pp. 934-945
Closed Access | Times Cited: 30
Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices
Tristan Maxson, Ademola Soyemi, Benjamin W. J. Chen, et al.
The Journal of Physical Chemistry C (2024) Vol. 128, Iss. 16, pp. 6524-6537
Open Access | Times Cited: 11
Strain-driven anisotropic enhancement in the thermal conductivity of KCaBi: the role of optical phonons
Xue-Kun Chen, Y. Zhang, Qing-Qing Luo, et al.
International Journal of Heat and Mass Transfer (2024) Vol. 236, pp. 126364-126364
Closed Access | Times Cited: 11
Computational insight of lithium adsorption and intercalation in bilayer TiC3
Jongee Park, Syeda Afrinish Fatima
Electrochimica Acta (2024) Vol. 477, pp. 143763-143763
Closed Access | Times Cited: 8
Machine Learning Accelerated Discovery of Functional MXenes with Giant Piezoelectric Coefficients
Xiaowen Li, Jian Qiu, Heping Cui, et al.
ACS Applied Materials & Interfaces (2024) Vol. 16, Iss. 10, pp. 12731-12743
Closed Access | Times Cited: 8
Navigating the Evolution of Carbon Nitride Research: Integrating Machine Learning into Conventional Approaches
Deep Mondal, Sujoy Datta, Debnarayan Jana
Physical Chemistry Chemical Physics (2025)
Closed Access | Times Cited: 1
Structural, electronic, and Li-ion adsorption properties of PolyPyGY explored by first-principles and machine learning simulations: A new multi-ringed 2D carbon allotrope
K. A. L. Lima, D. A. da Silva, Georges Daniel Amvame Nze, et al.
Journal of Energy Storage (2025) Vol. 117, pp. 116099-116099
Closed Access | Times Cited: 1
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