Mohammed Al‐Fahdi

440 total citations
17 papers, 315 citations indexed

About

Mohammed Al‐Fahdi is a scholar working on Materials Chemistry, Mechanics of Materials and Electrical and Electronic Engineering. According to data from OpenAlex, Mohammed Al‐Fahdi has authored 17 papers receiving a total of 315 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Materials Chemistry, 2 papers in Mechanics of Materials and 2 papers in Electrical and Electronic Engineering. Recurrent topics in Mohammed Al‐Fahdi's work include Machine Learning in Materials Science (11 papers), Advanced Thermoelectric Materials and Devices (7 papers) and Thermal properties of materials (6 papers). Mohammed Al‐Fahdi is often cited by papers focused on Machine Learning in Materials Science (11 papers), Advanced Thermoelectric Materials and Devices (7 papers) and Thermal properties of materials (6 papers). Mohammed Al‐Fahdi collaborates with scholars based in United States, China and Switzerland. Mohammed Al‐Fahdi's co-authors include Ming Hu, Tao Ouyang, Alejandro Rodriguez, Jianjun Hu, Yong Zhao, Xiaoliang Zhang, Nihang Fu, Zhenyao Wu, Edirisuriya M. Dilanga Siriwardane and Kunpeng Yuan and has published in prestigious journals such as Journal of Applied Physics, Journal of Materials Chemistry A and Cement and Concrete Research.

In The Last Decade

Mohammed Al‐Fahdi

14 papers receiving 305 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mohammed Al‐Fahdi United States 11 270 82 38 24 20 17 315
Alejandro Rodriguez United States 11 278 1.0× 85 1.0× 31 0.8× 21 0.9× 14 0.7× 14 329
Akitoshi Suzumura Japan 11 232 0.9× 156 1.9× 56 1.5× 20 0.8× 9 0.5× 23 341
Loitongbam Surajkumar Singh India 15 302 1.1× 176 2.1× 54 1.4× 24 1.0× 6 0.3× 33 418
Swanti Satsangi India 6 446 1.7× 147 1.8× 29 0.8× 7 0.3× 45 2.3× 8 493
Peiling Li China 11 268 1.0× 125 1.5× 54 1.4× 12 0.5× 7 0.3× 23 382
Tim Hsu United States 11 215 0.8× 122 1.5× 15 0.4× 32 1.3× 16 0.8× 22 288
Myriam Paire France 12 259 1.0× 351 4.3× 21 0.6× 49 2.0× 6 0.3× 34 432
Xiaohan Jia China 7 131 0.5× 217 2.6× 6 0.2× 15 0.6× 6 0.3× 15 288
Tian‐E Fan China 12 144 0.5× 271 3.3× 117 3.1× 4 0.2× 7 0.3× 28 428
Jean Spièce United Kingdom 10 237 0.9× 80 1.0× 17 0.4× 19 0.8× 2 0.1× 21 285

Countries citing papers authored by Mohammed Al‐Fahdi

Since Specialization
Citations

This map shows the geographic impact of Mohammed Al‐Fahdi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mohammed Al‐Fahdi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammed Al‐Fahdi more than expected).

Fields of papers citing papers by Mohammed Al‐Fahdi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohammed Al‐Fahdi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mohammed Al‐Fahdi. The network helps show where Mohammed Al‐Fahdi may publish in the future.

Co-authorship network of co-authors of Mohammed Al‐Fahdi

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammed Al‐Fahdi. A scholar is included among the top collaborators of Mohammed Al‐Fahdi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mohammed Al‐Fahdi. Mohammed Al‐Fahdi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Al‐Fahdi, Mohammed, et al.. (2025). A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons. arXiv (Cornell University). 1 indexed citations
2.
Rurali, Riccardo, et al.. (2025). Machine-learning-assisted discovery of lattice dynamics signatures of sodium superionic conductors. Materials Horizons. 12(24). 10864–10879.
3.
Al‐Fahdi, Mohammed, Riccardo Rurali, Jianjun Hu, Christopher Wolverton, & Ming Hu. (2025). Accelerated discovery of extreme lattice thermal conductivity by crystal graph attention networks and chemical bonding. npj Computational Materials. 12(1).
4.
Al‐Fahdi, Mohammed, Kunpeng Yuan, Yagang Yao, Riccardo Rurali, & Ming Hu. (2024). High-throughput thermoelectric materials screening by deep convolutional neural network with fused orbital field matrix and composition descriptors. Applied Physics Reviews. 11(2). 17 indexed citations
5.
Al‐Fahdi, Mohammed, Changpeng Lin, Chen Shen, Hongbin Zhang, & Ming Hu. (2024). Rapid prediction of phonon density of states by crystal attention graph neural network and high-throughput screening of candidate substrates for wide bandgap electronic cooling. Materials Today Physics. 50. 101632–101632. 5 indexed citations
6.
Al‐Fahdi, Mohammed, et al.. (2024). Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage. Journal of Materials Chemistry A. 12(14). 8502–8515. 10 indexed citations
7.
Al‐Fahdi, Mohammed & Ming Hu. (2024). High throughput substrate screening for interfacial thermal management of β-Ga2O3 by deep convolutional neural network. Journal of Applied Physics. 135(20). 6 indexed citations
8.
Rodriguez, Alejandro, Changpeng Lin, Mohammed Al‐Fahdi, et al.. (2023). Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table. npj Computational Materials. 9(1). 21 indexed citations
9.
Zhao, Yong, Edirisuriya M. Dilanga Siriwardane, Zhenyao Wu, et al.. (2023). Physics guided deep learning for generative design of crystal materials with symmetry constraints. npj Computational Materials. 9(1). 68 indexed citations
10.
Rodriguez, Alejandro, Changpeng Lin, Chen Shen, et al.. (2023). Unlocking phonon properties of a large and diverse set of cubic crystals by indirect bottom-up machine learning approach. Communications Materials. 4(1). 20 indexed citations
11.
Chang, Zheng, Jing Ma, Kunpeng Yuan, et al.. (2022). Zintl Phase Compounds Mg3Sb2−xBix (x = 0, 1, and 2) Monolayers: Electronic, Phonon and Thermoelectric Properties From ab Initio Calculations. Frontiers in Mechanical Engineering. 8. 16 indexed citations
12.
Chang, Zheng, Kunpeng Yuan, Jiale Li, et al.. (2022). Anomalous Thermal Conductivity Induced by High Dispersive Optical Phonons in Rubidium and Cesium Halides. ES Energy & Environments. 11 indexed citations
13.
Al‐Fahdi, Mohammed, et al.. (2022). Efficiently searching extreme mechanical properties via boundless objective-free exploration and minimal first-principles calculations. npj Computational Materials. 8(1). 14 indexed citations
14.
Al‐Fahdi, Mohammed, Alejandro Rodriguez, Tao Ouyang, & Ming Hu. (2021). High-Throughput Computation of New Carbon Allotropes with Diverse Hybridization and Ultrahigh Hardness. Crystals. 11(7). 783–783. 63 indexed citations
15.
Al‐Fahdi, Mohammed, Xiaoliang Zhang, & Ming Hu. (2021). Phonon transport anomaly in metavalent bonded materials: contradictory to the conventional theory. Journal of Materials Science. 56(33). 18534–18549. 18 indexed citations
16.
Al‐Fahdi, Mohammed, et al.. (2021). Experimental and computational characterization of glass microsphere-cementitious composites. Cement and Concrete Research. 152. 106671–106671. 13 indexed citations
17.
Al‐Fahdi, Mohammed, Tao Ouyang, & Ming Hu. (2021). High-throughput computation of novel ternary B–C–N structures and carbon allotropes with electronic-level insights into superhard materials from machine learning. Journal of Materials Chemistry A. 9(48). 27596–27614. 32 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026