2 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
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…
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…