• 1082 Citations
  • 15 h-Index
20052019
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Fingerprint Dive into the research topics where Fahhad Alharbi is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 2 Similar Profiles
Solar cells Engineering & Materials Science
solar cells Physics & Astronomy
Cations Chemical Compounds
Perovskite Engineering & Materials Science
Lead Engineering & Materials Science
Optical waveguides Engineering & Materials Science
perovskites Physics & Astronomy
Conversion efficiency Engineering & Materials Science

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Research Output 2005 2019

  • 1082 Citations
  • 15 h-Index
  • 69 Article
  • 12 Conference contribution
  • 2 Comment/debate
  • 1 Chapter
Physical Chemistry
Physical chemistry
machine learning
physical chemistry
Physics

Efficient high order method for differential equations in unbounded domains using generalized coordinate transformation

Mumtaz, F., Saidaoui, H. & Alharbi, F., 15 Mar 2019, In : Journal of Computational Physics. 381, p. 275-289 15 p.

Research output: Contribution to journalArticle

coordinate transformations
Differential equations
differential equations
sine series
Boundary conditions
2 Citations (Scopus)

Elucidating the role of interfacial MoS 2 layer in Cu 2 ZnSnS 4 thin film solar cells by numerical analysis

Ferdaous, M. T., Shahahmadi, S. A., Chelvanathan, P., Akhtaruzzaman, M., Alharbi, F., Sopian, K., Tiong, S. K. & Amin, N., 15 Jan 2019, In : Solar Energy. p. 162-172 11 p.

Research output: Contribution to journalArticle

Numerical analysis
Electron affinity
Doping (additives)
Solar cells
Energy gap

Enhancing the electronic dimensionality of hybrid organic-inorganic frameworks by hydrogen bonded molecular cations

El-Mellouhi, F., Madjet, M., Berdiyorov, G., Bentria, E. T., Rashkeev, S., Kais, S., Akande, A., Motta, C., Sanvito, S. & Alharbi, F., 1 Jul 2019, In : Materials Horizons. 6, 6, p. 1187-1196 10 p.

Research output: Contribution to journalArticle

Vanadates
Optoelectronic devices
Cations
Hydrogen
Positive ions
4 Citations (Scopus)

Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning

Park, H., Mall, R., Alharbi, F., Sanvito, S., Tabet, N., Bensmail, H. & El-Mellouhi, F., 1 Jan 2019, In : Physical Chemistry Chemical Physics. 21, 3, p. 1078-1088 11 p.

Research output: Contribution to journalArticle

machine learning
perovskites
Formability
Discrete Fourier transforms
learning