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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:

Recent advances in lattice thermal conductivity calculation using machine-learning interatomic potentials
Saeed Arabha, Zahra Shokri Aghbolagh, Khashayar Ghorbani, et al.
Journal of Applied Physics (2021) Vol. 130, Iss. 21
Open Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

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
Predicting lattice thermal conductivity via machine learning: a mini review
Yufeng Luo, Mengke Li, Hongmei Yuan, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 51
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
Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
Bohayra Mortazavi
Advanced Energy Materials (2024)
Open Access | Times Cited: 16
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
A machine-learning-based investigation on the mechanical/failure response and thermal conductivity of semiconducting BC2N monolayers
Bohayra Mortazavi, Ivan S. Novikov, Alexander V. Shapeev
Carbon (2021) Vol. 188, pp. 431-441
Closed Access | Times Cited: 53
Anisotropic and outstanding mechanical, thermal conduction, optical, and piezoelectric responses in a novel semiconducting BCN monolayer confirmed by first-principles and machine learning
Bohayra Mortazavi, Fazel Shojaei, M. Yagmurcukardes, et al.
Carbon (2022) Vol. 200, pp. 500-509
Open Access | Times Cited: 35
Thermal transports of 2D phosphorous carbides by machine learning molecular dynamics simulations
Chenyang Cao, Shuo Cao, Yuan-Xu Zhu, et al.
International Journal of Heat and Mass Transfer (2024) Vol. 224, pp. 125359-125359
Closed Access | Times Cited: 6
Low and Anisotropic Tensile Strength and Thermal Conductivity in the Single-Layer Fullerene Network Predicted by Machine-Learning Interatomic Potentials
Bohayra Mortazavi, Xiaoying Zhuang
Coatings (2022) Vol. 12, Iss. 8, pp. 1171-1171
Open Access | Times Cited: 21
Ultrahigh strength and negative thermal expansion and low thermal conductivity in graphyne nanosheets confirmed by machine-learning interatomic potentials
Bohayra Mortazavi, Xiaoying Zhuang
FlatChem (2022) Vol. 36, pp. 100446-100446
Closed Access | Times Cited: 19
A moment tensor potential for lattice thermal conductivity calculations of α and β phases of Ga2O3
Nikita Rybin, Alexander V. Shapeev
Journal of Applied Physics (2024) Vol. 135, Iss. 20
Open Access | Times Cited: 4
Reviewing numerical studies on sensible thermal energy storage in cementitious composites: report of the RILEM TC 299-TES
Mohammad Rahjoo, António Caggiano, Umberto Berardi, et al.
Materials and Structures (2025) Vol. 58, Iss. 1
Open Access
Lattice thermal conductivity of 8-16-4(sun)-graphyne from reverse nonequilibrium molecular dynamics simulations
Isaac M. Félix, Raphael M. Tromer, Leonardo D. Machado, et al.
International Journal of Heat and Mass Transfer (2025) Vol. 241, pp. 126746-126746
Closed Access
Heat transfer enhancement of N-Ga-Al semiconductors heterogeneous interfaces
Wenzhu Luo, Ershuai Yin, Lei Wang, et al.
International Journal of Heat and Mass Transfer (2025) Vol. 244, pp. 126902-126902
Closed Access
Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential
Ali Rajabpour, Bohayra Mortazavi, Pedram Mirchi, et al.
International Journal of Thermal Sciences (2025) Vol. 214, pp. 109876-109876
Open Access
Thermal and mechanical properties of deep-ultraviolet light sources candidate materials BeGeN2 by machine-learning molecular dynamics simulations
Zhendong Li, Longwei Han, Tao Ouyang, et al.
Physical Review Materials (2025) Vol. 9, Iss. 3
Closed Access
Recent Progress in the Design and Application of Machine Learning for the Hydrogen Evolution Reaction in Electrocatalysis and Photocatalysis
Kaifeng Zhang, Xudong Wang, Yanjing Su
Materials Today Communications (2025), pp. 112462-112462
Closed Access
Estimating the lattice thermal conductivity of AlCoCrNiFe high-entropy alloy using machine learning
Jie Lu, Xiaona Huang, Yanan Yue
Journal of Applied Physics (2024) Vol. 135, Iss. 13
Open Access | Times Cited: 3
Harnessing BN Co-doping for SuperiorThermal Transport in PhagrapheneMonolayer
Asfakujjaman, Mainak Ghosh, Suman Chowdhury, et al.
Journal of Physics D Applied Physics (2024) Vol. 57, Iss. 40, pp. 405301-405301
Closed Access | Times Cited: 3
First-principles and machine-learning study of electronic and phonon transport in carbon-based AA-stacked bilayer biphenylene nanosheets
Suman Chowdhury, Supriya Ghosal, Deep Mondal, et al.
Journal of Physics and Chemistry of Solids (2022) Vol. 170, pp. 110909-110909
Closed Access | Times Cited: 15
Promising novel thermoelectric materials: two-dimensional penta-like PtPX (X = S, Se, Te) nanosheets
Haihua Huang, Wei Li, Chengchao Hu, et al.
Journal of Materials Chemistry C (2023) Vol. 11, Iss. 28, pp. 9449-9464
Closed Access | Times Cited: 8
Applications and training sets of machine learning potentials
Chang‐Ho Hong, Jaehoon Kim, Jaesun Kim, et al.
Science and Technology of Advanced Materials Methods (2023) Vol. 3, Iss. 1
Open Access | Times Cited: 8
Exploring thermal properties of PbSnTeSe and PbSnTeS high entropy alloys with machine-learned potentials
Chun-Ming Chang
Modelling and Simulation in Materials Science and Engineering (2024) Vol. 32, Iss. 3, pp. 035008-035008
Open Access | Times Cited: 2
Machine-learning-derived thermal conductivity of two-dimensional TiS2/MoS2 van der Waals heterostructures
Akhil K. Nair, Carlos Da Silva, Cristina H. Amon
APL Machine Learning (2024) Vol. 2, Iss. 3
Open Access | Times Cited: 2
Machine-learned interatomic potentials for accurate analysis of the mechanical properties of boron nitride sheets
Vijay Choyal, Mahesh Pralhad Patil, Nitin Luhadiya, et al.
Journal of Physics Materials (2024) Vol. 8, Iss. 1, pp. 015003-015003
Open Access | Times Cited: 2
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