Ferenc Karsai

VASP Software (Austria) — Austria

Materials Science · Physical Sciences

16h-index2.3kcitations32works5.42yr avg
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Research Topics

Machine Learning in Materials Science(17), Advanced Chemical Physics Studies(11), Computational Drug Discovery Methods(7), Electronic and Structural Properties of Oxides(5), Electrochemical Analysis and Applications(4)

Publications32 total

Efficient periodic density functional theory calculations of charged molecules and surfaces using Coulomb kernel truncation
Physical review. B./Physical review. B·2025· 2 citedOpen Access
Efficient periodic density functional theory calculations of charged molecules and surfaces using Coulomb kernel truncation
arXiv (Cornell University)·2025· 2 citedOpen Access
Correction: Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations
Chemical Science·2025· 1 citedOpen Access
Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations
Chemical Science·2024· 11 citedOpen Access
Author response for "Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations"
2024· 1 cited
Publisher Correction: Machine learning-aided first-principles calculations of redox potentials
npj Computational Materials·2024Open Access
Ab initio data to generates machine-learned force fields of ions in aqueous medium in VASP format.
arXiv (Cornell University)·2024Open Access
Machine learning-aided first-principles calculations of redox potentials
npj Computational Materials·2024· 26 citedOpen Access
Machine Learning-Aided First-Principles Calculations of Redox Potentials
arXiv (Cornell University)·2023Open Access
Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential
The Journal of Physical Chemistry Letters·2023· 21 cited
Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)
Physical Review Letters·2023· 47 cited
Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)
arXiv (Cornell University)·2022· 2 citedOpen Access
Phase transitions of zirconia: Machine-learned force fields beyond density functional theory
Physical review. B./Physical review. B·2022· 57 citedOpen Access
Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
arXiv (Cornell University)·2021Open Access
Thermal transport and phase transitions of zirconia by on-the-fly\n machine-learned interatomic potentials
arXiv (Cornell University)·2021Open Access
Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
npj Computational Materials·2021· 145 citedOpen Access
Thermodynamic properties by on-the-fly machine-learned interatomic potentials: thermal transport and phase transitions of zirconia
Bulletin of the American Physical Society·2021
First-principles hydration free energies of oxygenated species at water–platinum interfaces
The Journal of Chemical Physics·2021· 24 cited
On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations
The Journal of Physical Chemistry Letters·2020· 189 cited
Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials
The Journal of Chemical Physics·2020· 224 cited
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Frequent Co-authors

Georg Kresse(25), Ryosuke Jinnouchi(18), Carla Verdi(10), Peitao Liu(6), Peter Blaha(6), Fabien Tran(3), Ryoji Asahi(2), Espen Flage−Larsen(2), Paul Tiwald(2), Robert Laskowski(2), Stefanie Gräfe(2), Joachim Burgdörfer(2), Ludger Wirtz(2), Jian-Tao Wang(2), Noah Avargues(2), Andreas Singraber(2), Xing‐Qiu Chen(2), Sudarshan Vijay(2), Martin Schlipf(2), Henrique Miranda(2)