133 citations · 196 across the 12 of their papers we have counts for
13 papers
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
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…
Nuclear Quantum Effects in Liquid Water Are Negligible for Structure but Significant for Dynamics
Nore Stolte, János Daru, Harald Forbert +2
Isotopic substitution, which can be realized both in experiment and computer simulations, is a direct approach to assess the role of nuclear quantum effects on the structure and dy…
Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water
Nore Stolte, János Daru, Harald Forbert +2
Training accurate machine learning potentials requires electronic structure data comprehensively covering the configurational space of the system of interest. As the construction o…
A High-Dimensional Neural Network Potential for CoO
Amir Omranpour, Jörg Behler
The CoO spinel is an important material in oxidation catalysis. Its properties under catalytic conditions, i.e., at finite temperatures, can be studied by molecular dynamic…
From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron
Sarath Menon, Yury Lysogorskiy, Alexander L. M. Knoll +10
We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning P…
Ab initio quality neural-network potential for sodium
Hagai Eshet, Rustam Z. Khaliullin, Thomas D. Kuhne +2
An interatomic potential for high-pressure high-temperature (HPHT) crystalline and liquid phases of sodium is created using a neural-network (NN) representation of the ab initio po…