98 citations · 437 across the 27 of their papers we have counts for
4 papers · 1 filter
Band gap prediction for large organic crystal structures with machine learning
Bart Olsthoorn, R. Matthias Geilhufe, Stanislav S. Borysov +1
Machine-learning models are capable of capturing the structure-property relationship from a dataset of computationally demanding ab initio calculations. Over the past two years, th…
Chemical-Strain Induced Tilted Dirac Nodes in (BEDT-TTF)X (X = I, Cl, Br, F) Based Charge-Transfer Salts
R. Matthias Geilhufe, Benjamin Commeau, Gayanath W. Fernando
The identification of novel multifunctional Dirac materials has been an ongoing effort. In this connection quasi 2-dimensional (BEDT-TTF)-based charge transfer salts are widely dis…
Materials Informatics for Dark Matter Detection
R. Matthias Geilhufe, Bart Olsthoorn, Alfredo Ferella +4
Dark Matter particles are commonly assumed to be weakly interacting massive particles (WIMPs) with a mass in the GeV to TeV range. However, recent interest has shifted towards ligh…
Hund nodal line semimetals: The case of twisted magnetic phase in the double-exchange model
R. Matthias Geilhufe, Francisco Guinea, Vladimir Juricic
We propose a class of topological metals, which we dub \emph{Hund nodal line semimetals}, arising from the strong Coulomb interaction encoded in the Hund's coupling between itinera…