Konstantinos Gkagkas
Konstantinos Gkagkas
Toyota Motor Europe NV/SA
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Zitiert von
Zitiert von
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
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
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
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
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
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
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
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
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
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
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
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
Interdependence of amplitude roughness parameters on rough Gaussian surfaces
SK Fecske, K Gkagkas, C Gachot, A Vernes
Tribology Letters 68, 1-15, 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
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
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
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
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
Mechanical characterization and induced crystallization in nanocomposites of thermoplastics and carbon nanotubes
ER Cruz-Chú, GJ Villegas-Rodríguez, T Jäger, L Valentini, NM Pugno, ...
npj Computational Materials 6 (1), 151, 2020
Stress-induced long-range ordering in spider silk
JA Wagner, SP Patil, I Greving, M Lämmel, K Gkagkas, T Seydel, M Müller, ...
Scientific Reports 7 (1), 15273, 2017
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