Konstantinos Gkagkas

Toyota Motor Corporation (Switzerland) — Switzerland

Materials Science · Physical Sciences

15h-index873citations42works3.02yr avg
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Research Topics

Machine Learning in Materials Science(13), Metal-Organic Frameworks: Synthesis and Applications(11), Force Microscopy Techniques and Applications(6), Lubricants and Their Additives(6), Combustion and flame dynamics(4)

Publications42 total

RetNeXt: A Pretrained Model for Transfer Learning Across the MOF Adsorption Space
Journal of Chemical Information and Modeling·2026Open Access
Learning Density Functionals to Bridge Particle and Continuum Scales
ArXiv.org·2025Open Access
Learning Density Functionals to Bridge Particle and Continuum Scales
arXiv (Cornell University)·2025Open Access
Gas diffusion layer descriptors identified by machine learning to predict electrical contact resistance
Modelling and Simulation in Materials Science and Engineering·2025Open Access
Greenwood-Tripp model: A bibliometric overview
Tribology and Materials·2025Open Access
Gas adsorption meets geometric deep learning: points, set and match
Scientific Reports·2024· 4 citedOpen Access
Gas adsorption meets deep learning: voxelizing the potential energy surface of metal-organic frameworks
Scientific Reports·2024· 17 citedOpen Access
Comparison of machine learning approaches for the identification of top-performing materials for hydrogen storage
Sustainable Chemistry for the Environment·2023· 4 citedOpen Access
Comparison of Energy-Based Machine Learning Descriptors for Gas Adsorption
The Journal of Physical Chemistry C·2023· 12 cited
Mechanical Properties of Twisted Carbon Nanotube Bundles with Carbon Linkers from Molecular Dynamics Simulations
International Journal of Molecular Sciences·2023· 11 citedOpen Access
RetNet
Figshare·2023Open Access
Relating Dry Friction to Interdigitation of Surface Passivation Species: A Molecular Dynamics Study on Amorphous Carbon
Materials·2022· 9 citedOpen Access
Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage
The Journal of Chemical Physics·2022· 19 cited
1,2,3-Trimethoxypropane: a bio-sourced glyme as electrolyte for lithium–O<sub>2</sub> batteries
Green Chemistry·2022· 7 cited
Statistics of Sliding on Periodic and Atomically Flat Surfaces
Frontiers in Mechanical Engineering·2021Open Access
A combination of multi-scale calculations with machine learning for investigating hydrogen storage in metal organic frameworks
International Journal of Hydrogen Energy·2021· 52 cited
Longitudinal strand ordering leads to shear thinning in Nafion
Physical Chemistry Chemical Physics·2021· 11 citedOpen Access
Mechanical characterization and induced crystallization in nanocomposites of thermoplastics and carbon nanotubes
npj Computational Materials·2020· 12 citedOpen Access
Fast Screening of Large Databases for Top Performing Nanomaterials Using a Self-Consistent, Machine Learning Based Approach
The Journal of Physical Chemistry C·2020· 30 cited
Evolutionary Algorithm Optimization of Staggered Biological or Biomimetic Composites Using the Random Fuse Model
Physical Review Applied·2020· 8 citedOpen Access
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Frequent Co-authors

George E. Froudakis(14), George S. Fanourgakis(9), Emmanuel Tylianakis(7), Antonios P. Sarikas(6), A. Vernes(5), Frauke Gräter(5), R.P. Lindstedt(3), Varvara Asouti(3), Carsten Gachot(3), Sandeep P. Patil(3), Eduardo R. Cruz-Chú(3), Nicola M. Pugno(3), Emmanuel Klontzas(2), Marco Di Gennaro(2), E.M. Papoutsis‐Kiachagias(2), Kyriakos C. Giannakoglou(2), Veerapandian Ponnuchamy(2), Miljan Dašić(2), Igor Stanković(2), Ulrike Cihak-Bayr(2)