3 papers
cond-mat.supr-con2026
Proximity-induced unconventional superconductivity and chiral topological phases in twisted graphene/NbSe van der Waals heterostructure
Adam Hložný, Marko MilivojeviÄ
We study proximity-induced unconventional superconductivity in a twisted graphene/NbSe van der Waals heterostructure using the Bogoliubov-de Gennes formalism. The normal-state…
cond-mat.mtrl-sci2026
Upscaling DFT-trained machine-learning interatomic potential toward Quantum Monte Carlo accuracy: Sulfur-vacancy migration in monolayer MoS as a testbed
Adam Hložný, Ján Brndiar, Ye Luo +1
We designed a procedure to train a machine learning interatomic potential (MLIP) at benchmark-quality quantum Monte Carlo (QMC) accuracy. To avoid the complexities of high-quality…
cond-mat.supr-con2026
Machine learning protocol to identify pairing symmetries via quasiparticle interference imaging in Ising superconductors
Adam Hložný, Adam Hložný, Jozef HaniÅ¡ +4
Identifying the pairing symmetry in unconventional superconductors is essential for reliably characterizing their superconducting states and for enabling their integration into rea…