Scopus
EXPORT Fanourgakis G.

Karatasos K., Fanourgakis G.S., Zuburtikudis I., Khalifeh H.A.
Mechanisms determining water permeability and ion selectivity of multilayered glycine-functionalized graphene oxide membranes in saline water reverse osmosis processes: A computational investigation
(2025) Journal of Membrane Science, 713, art. no. 123361, Cited 5 times.
DOI: 10.1016/j.memsci.2024.123361

Iskandarov J., Fanourgakis G.S., Ahmed S., Alameri W., Froudakis G.E., Karanikolos G.N.
Machine learning prediction and optimization of CO<sub>2</sub> foam performance for enhanced oil recovery and carbon sequestration: Effect of surfactant type and operating conditions
(2024) Geoenergy Science and Engineering, 240, art. no. 213064, Cited 8 times.
DOI: 10.1016/j.geoen.2024.213064

Iskandarov J., Fanourgakis G., Alameri W., Froudakis G., Karanikolos G.
Machine Learning Application to CO<sub>2</sub> Foam Rheology
(2021) Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021, Cited 5 times.
DOI: 10.2118/208016-MS

Karatasos K., Fanourgakis G.S., Zuburtikudis I., Khalifeh H.A.
Multilayer graphene oxide-based membranes for reverse osmosis water desalination: An atomistically detailed simulation study
(2023) Journal of Environmental Chemical Engineering, 11 (5), art. no. 110550, Cited 8 times.
DOI: 10.1016/j.jece.2023.110550

Zhang L., Allendorf M.D., Balderas-Xicohténcatl R., Broom D.P., Fanourgakis G.S., Froudakis G.E., Gennett T., Hurst K.E., Ling S., Milanese C., Parilla P.A., Pontiroli D., Riccò M., Shulda S., Stavila V., Steriotis T.A., Webb C.J., Witman M., Hirscher M.
Fundamentals of hydrogen storage in nanoporous materials
(2022) Progress in Energy, 4 (4), art. no. 042013, Cited 77 times.
DOI: 10.1088/2516-1083/ac8d44

Sarikas A.P., Fanourgakis G.S., Gkagkas K., Froudakis G.E.
Comparison of machine learning approaches for the identification of top-performing materials for hydrogen storage
(2024) Sustainable Chemistry for the Environment, 5, art. no. 100056, Cited 6 times.
DOI: 10.1016/j.scenv.2023.100056

Fanourgakis G.S., Gkagkas K., Froudakis G.
Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage
(2022) Journal of Chemical Physics, 156 (5), art. no. 054103, Cited 18 times.
DOI: 10.1063/5.0075994

Iskandarov J., Ahmed S., Fanourgakis G.S., Alameri W., Froudakis G.E., Karanikolos G.N.
Predicting and optimizing CO<sub>2</sub> foam performance for enhanced oil recovery: A machine learning approach to foam formulation focusing on apparent viscosity and interfacial tension
(2024) Marine and Petroleum Geology, 170, art. no. 107108, Cited 13 times.
DOI: 10.1016/j.marpetgeo.2024.107108

Iskandarov J., Fanourgakis G.S., Ahmed S., Alameri W., Froudakis G.E., Karanikolos G.N.
Data-driven prediction of in situ CO<sub>2</sub> foam strength for enhanced oil recovery and carbon sequestration
(2022) RSC Advances, 12 (55), pp. 35703 - 35711, Cited 20 times.
DOI: 10.1039/d2ra05841c

Chatziparaschos M., Daskalakis N., Myriokefalitakis S., Fanourgakis G., Kanakidou M.
Global Simulations of Ice Nuclei Particles Derived from Organics and Inorganics Particles
(2021) Springer Proceedings in Complexity, pp. 19 - 24, Cited 0 times.
DOI: 10.1007/978-3-662-63760-9_3

Sarikas A.P., Fanourgakis G.S., Froudakis G.E.
Metal-organic frameworks in the age of machine learning
(2023) Reticular Chemistry and Applications: Metal-Organic Frameworks, pp. 43 - 72, Cited 0 times.
DOI: 10.1515/9781501524721-003

Manitsas L., Fanourgakis G.S.
A physically motivated Machine Learning model for accurate gas adsorption predictions in nanoporous materials
(2025) Microporous and Mesoporous Materials, 398, art. no. 113796, Cited 4 times.
DOI: 10.1016/j.micromeso.2025.113796

Sarikas A.P., Fanourgakis G.S., Tylianakis E., Gkagkas K., Froudakis G.E.
Comparison of Energy-Based Machine Learning Descriptors for Gas Adsorption
(2023) Journal of Physical Chemistry C, 127 (43), pp. 20995 - 21005, Cited 11 times.
DOI: 10.1021/acs.jpcc.3c04223