2 papers
cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2025
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…