133 citations · 196 across the 12 of their papers we have counts for
7 papers · 1 filter
A Combined Theoretical and Experimental Study of Oxygen Vacancies in CoO for Liquid-Phase Oxidation Catalysis
Amir Omranpour, Lea Kämmerer, Catalina Leiva-Leroy +12
In the present work, we investigate oxygen vacancies (V) in CoO, both in the bulk phase and under liquid-phase ethylene glycol oxidation, by combining theoreti…
Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials
Jan Elsner, K Nikolas Lausch, Jörg Behler
Oxide-water interfaces govern a wide range of physical and chemical processes fundamental to many fields like catalysis, geochemistry, corrosion, electrochemistry, and sensor techn…
Computation of the heat capacity of water from first principles
Motoyuki Shiga, Jan Elsner, Jörg Behler +1
Water is a unique solvent with many remarkable properties. An example is its exceptionally high heat capacity, which plays an important role in storing and transporting thermal ene…
Insights into the Structure and Dynamics of Water at CoO(001) Using a High-Dimensional Neural Network Potential
Amir Omranpour, Jörg Behler
CoO is an important catalyst for the oxidation of organic molecules in the liquid phase. Still, understanding the atomistic details of CoO-water interfaces under op…
Impact of the damping function in dispersion-corrected density functional theory on the properties of liquid water
K. Nikolas Lausch, Redouan El Haouari, Daniel Trzewik +1
Accounting for dispersion interactions is essential in approximate density functional theory (DFT). Often, a correction potential based on the London formula is added, which is dam…
Iterative charge equilibration for fourth-generation high-dimensional neural network potentials
Emir Kocer, Andreas Singraber, Jonas A. Finkler +4
Machine learning potentials (MLP) allow to perform large-scale molecular dynamics simulations with about the same accuracy as electronic structure calculations provided that the se…
Free energy profiles for chemical reactions in solution from high-dimensional neural network potentials: The case of the Strecker synthesis
Alea Miako Tokita, Timothée Devergne, A. Marco Saitta +1
Machine learning potentials (MLPs) have become a popular tool in chemistry and materials science as they combine the accuracy of electronic structure calculations with the high com…