2 citations · 4 across the 8 of their papers we have counts for
8 papers
Dynamical properties of ab initio water from machine-learning potentials
P. Montero de Hijes, L. Neubeck, G. Kresse +1
We assess the dynamical properties of liquid water predicted by several density functionals using machine-learning interatomic potentials. MACE models were trained for SCAN, RPBE-D…
Scalar machine learning of tensorial quantities -- Born effective charges from monopole models
Bernhard Schmiedmayer, Angela Rittsteuer, Tobias Hilpert +1
Predicting tensorial properties with machine learning models typically requires carefully designed tensorial descriptors. In this work, we introduce an alternative strategy for lea…
Accurate Thermophysical Properties of Water using Machine-Learned Potentials
Tobias Hilpert, Georg Kresse
Simulating water from first principles remains a significant computational challenge due to the slow dynamics of the underlying system. Although machine-learned interatomic potenti…
Equivariant machine learning of Electric Field Gradients -- Predicting the quadrupolar coupling constant in the MAPbI phase transition
Bernhard Schmiedmayer, J. W. Wolffs, Gilles A. de Wijs +3
We present a strategy combining machine learning and first-principles calculations to achieve highly accurate nuclear quadrupolar coupling constant predictions. Our approach employ…
Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations
Ryosuke Jinnouchi, Ferenc Karsai, Georg Kresse
Constructing a self-consistent first-principles framework that accurately predicts the properties of electron transfer reactions through finite-temperature molecular dynamics simul…
Structure and dynamics of the magnetite(001)/water interface from molecular dynamics simulations based on a neural network potential
Salvatore Romano, Pablo Montero de Hijes, Matthias Meier +3
The magnetite/water interface is commonly found in nature and plays a crucial role in various technological applications. However, our understanding of its structural and dynamical…
Derivative learning of tensorial quantities -- Predicting finite temperature infrared spectra from first principles
Bernhard Schmiedmayer, Georg Kresse
We develop a strategy that integrates machine learning and first-principles calculations to achieve technical accurate predictions of infrared spectra. Specifically, the methodolog…
Comparing machine learning potentials for water: Kernel-based regression and Behler-Parrinello neural networks
Pablo Montero de Hijes, Christoph Dellago, Ryosuke Jinnouchi +2
In this paper we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE+D3. Specifically, we scr…