| A universal machine learning algorithm for large-scale screening of materials GS Fanourgakis, K Gkagkas, E Tylianakis, GE Froudakis Journal of the American Chemical Society 142 (8), 3814-3822, 2020 | 155 | 2020 |
| Transported PDF modelling with detailed chemistry of pre-and auto-ignition in CH4/air mixtures K Gkagkas, RP Lindstedt Proceedings of the Combustion Institute 31 (1), 1559-1566, 2007 | 98 | 2007 |
| A robust machine learning algorithm for the prediction of methane adsorption in nanoporous materials GS Fanourgakis, K Gkagkas, E Tylianakis, E Klontzas, G Froudakis The Journal of Physical Chemistry A 123 (28), 6080-6087, 2019 | 81 | 2019 |
| A generic machine learning algorithm for the prediction of gas adsorption in nanoporous materials GS Fanourgakis, K Gkagkas, E Tylianakis, G Froudakis The Journal of Physical Chemistry C 124 (13), 7117-7126, 2020 | 80 | 2020 |
| An automated machine learning architecture for the accelerated prediction of metal-organic frameworks performance in energy and environmental applications I Tsamardinos, GS Fanourgakis, E Greasidou, E Klontzas, K Gkagkas, ... Microporous and Mesoporous Materials 300, 110160, 2020 | 54 | 2020 |
| Multi-point aerodynamic shape optimization of cars based on continuous adjoint EM Papoutsis-Kiachagias, VG Asouti, KC Giannakoglou, K Gkagkas, ... Structural and Multidisciplinary Optimization 59, 675-694, 2019 | 48 | 2019 |
| A combination of multi-scale calculations with machine learning for investigating hydrogen storage in metal organic frameworks RM Giappa, E Tylianakis, M Di Gennaro, K Gkagkas, GE Froudakis International Journal of Hydrogen Energy 46 (54), 27612-27621, 2021 | 40 | 2021 |
| The impact of reduced chemistry on auto-ignition of H2 in turbulent flows K Gkagkas, RP Lindstedt Combustion Theory and Modelling 13 (4), 607-643, 2009 | 36 | 2009 |
| Fast screening of large databases for top performing nanomaterials using a self-consistent, machine learning based approach GS Fanourgakis, K Gkagkas, E Tylianakis, G Froudakis The Journal of Physical Chemistry C 124 (36), 19639-19648, 2020 | 30 | 2020 |
| Molecular dynamics investigation of a model ionic liquid lubricant for automotive applications K Gkagkas, V Ponnuchamy, M Dašić, I Stanković Tribology International 113, 83-91, 2017 | 30 | 2017 |
| Methods for atomistic abrasion simulations of laterally periodic polycrystalline substrates with fractal surfaces SJ Eder, D Bianchi, U Cihak-Bayr, K Gkagkas Computer Physics Communications 212, 100-112, 2017 | 26 | 2017 |
| Interdependence of amplitude roughness parameters on rough Gaussian surfaces SK Fecske, K Gkagkas, C Gachot, A Vernes Tribology Letters 68, 1-15, 2020 | 24 | 2020 |
| Molecular dynamics investigation of the influence of the shape of the cation on the structure and lubrication properties of ionic liquids M Dašić, I Stanković, K Gkagkas Physical Chemistry Chemical Physics 21 (8), 4375-4386, 2019 | 23 | 2019 |
| Viscous friction between crystalline and amorphous phase of dragline silk SP Patil, S Xiao, K Gkagkas, B Markert, F Gräter PLoS One 9 (8), e104832, 2014 | 22 | 2014 |
| Transported PDF modelling of a high velocity bluff-body stabilised flame (HM2) using detailed chemistry K Gkagkas, RP Lindstedt, TS Kuan Flow, turbulence and combustion 82, 493-509, 2009 | 22 | 2009 |
| Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage GS Fanourgakis, K Gkagkas, G Froudakis The Journal of Chemical Physics 156 (5), 2022 | 12 | 2022 |
| Influence of confinement on flow and lubrication properties of a salt model ionic liquid investigated with molecular dynamics M Dašić, I Stanković, K Gkagkas The European Physical Journal E 41, 1-12, 2018 | 11 | 2018 |
| Mechanical properties of twisted carbon nanotube bundles with carbon linkers from molecular dynamics simulations A Pedrielli, M Dapor, K Gkagkas, S Taioli, NM Pugno International Journal of Molecular Sciences 24 (3), 2473, 2023 | 9 | 2023 |
| Relating dry friction to interdigitation of surface passivation species: A molecular dynamics study on amorphous carbon K Falk, T Reichenbach, K Gkagkas, M Moseler, G Moras Materials 15 (9), 3247, 2022 | 9 | 2022 |
| Gas adsorption meets deep learning: voxelizing the potential energy surface of metal-organic frameworks AP Sarikas, K Gkagkas, GE Froudakis Scientific Reports 14 (1), 2242, 2024 | 8 | 2024 |
George FroudakisUniversity of CreteVerified email at uoc.gr
