20 citations · 28 across the 10 of their papers we have counts for
23 papers · 1 filter
Prescreening Point Defects in Semiconductors With Machine Learning
Paul Karlsson, Joel Davidsson, Rickard Armiento
High-throughput calculations using density-functional theory (DFT) are commonly used to explore point defects for applications in power electronics and quantum technologies. There…
Large spin splitting metallic altermagnets from machine-learned design rules
Ali Sufyan, Brahim Marfoua, J. Andreas Larsson +2
Altermagnets combine compensated magnetic order with momentum-dependent spin splitting, providing spin-polarized electronic states without a net magnetization. Metallic -wave al…
Color Centers in Cubic Boron Nitride
William Stenlund, Joel Davidsson, Viktor Ivády +2
Cubic boron nitride (c-BN) is a wide-bandgap semiconductor (WBGS) with potential applications in both power electronics and quantum technologies. Color centers in WBGS can be used…
Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials
Abhijith S Parackal, Rickard Armiento, Florian Trybel
Machine learned interatomic potentials (MLIPs) are becoming a standard method for DFT-level accurate molecular dynamics simulation and large-scale studies of crystal energetics. In…
Screening 39 billion protostructures for materials discovery
Abhijith S Parackal, Florian Trybel, Felix Andreas Faber +1
Large-scale computational surveys are increasingly used to map the landscape of stable crystalline materials. We report a high-throughput energy screening of inorganic crystals tha…
High-Throughput Quantification of Altermagnetic Band Splitting
Ali Sufyan, Brahim Marfoua, J. Andreas Larsson +2
Altermagnetism represents a recently established class of collinear magnetism that combines zero net magnetization with momentum-dependent spin polarization, enabled by symmetry co…