arXiv subjects

Adam Hložný

Publications and source records attributed to Adam Hložný.

3 recordsLinked to original sources

arXiv2026

Machine learning protocol to identify pairing symmetries via quasiparticle interference imaging in Ising superconductors

Identifying the pairing symmetry in unconventional superconductors is essential for reliably characterizing their superconducting states and for enabling their integration into realistic quantum devices. Here, we introduce a machine-learning-guided strategy to determine pairing symmetry from quasiparticle interference (QPI) data, which integrates first-principles calculations, tight-binding modeling, and symmetry-based classification of the superconducting pairing function. We demonstrate the approach on monolayer NbSe2 as an experimentally accessible probe of superconductivity in real materials, within a single scalar-impurity Bogoliubov-de Gennes framework. Our analysis shows that the QPI-to-parameter inverse problem can be solved with high accuracy for most superconducting pairing channels in this setting, indicating that QPI carries rich, learnable information about the superconducting gap structure. Taken together, these results demonstrate that machine-learning-assisted QPI analysis provides a promising pathway for precise learning of superconducting pairing functions in quantum materials.

cond-mat.supr-con↗

arXiv2026

Proximity-induced unconventional superconductivity and chiral topological phases in twisted graphene/NbSe$_2$ van der Waals heterostructure

We study proximity-induced unconventional superconductivity in a twisted graphene/NbSe$_2$ van der Waals heterostructure using the Bogoliubov-de Gennes formalism. The normal-state parameters of proximitized graphene are extracted from ab initio calculations at a twist angle of $23.4^\circ$, which reduces the common symmetry of the heterostructure to $\mathbf{C}_3$. We construct symmetry-allowed superconducting gap functions of the graphene layer according to the irreducible representations of the $\mathbf{C}_3$ group, containing singlet and triplet pairing channels and their mixtures. Computing the topological invariants as a function of the mixing parameters, we find a rich phase diagram of chiral topological superconducting phases, characterized by nonzero Chern numbers $C\in\{-4,-2,2,4\}$. While the nature of the superconducting order parameter of NbSe$_2$ remains debated, the formation of the van der Waals heterostructure and the related symmetry reduction can alter the relative stability of competing pairing channels, potentially stabilizing a chiral component that is proximity-induced into graphene and triggers the topological phases identified here, making the twisted graphene/NbSe$_2$ heterostructure a promising platform for chiral topological superconductivity detectable via quasiparticle interference imaging and transport measurements.

cond-mat.supr-con↗

arXiv2026

Upscaling DFT-trained machine-learning interatomic potential toward Quantum Monte Carlo accuracy: Sulfur-vacancy migration in monolayer MoS$_2$ as a testbed

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 atomic force determination with the stochastic QMC methods, we use a multi-fidelity approach wherein high-level QMC energies are used alongside suitably processed low-level DFT atomic forces to train a QMC fine-tuned MLIP which significantly improves both the energetics and atomic forces over the baseline DFT-based MLIP. Fine-tuning is only applied to the readout layers of an equivariant message-passing MACE MLIP. We used sulfur mono- and multiple vacancies in monolayer MoS$_2$ as a testbed and demonstrate a near QMC accuracy of the model in a number of in- and out-of-domain tests. We show that a fairly limited dataset of QMC energies suffice to significantly improve the baseline DFT MLIP. The accuracy of our approach is demonstrated on energy and free energy migration barriers of mono- and multiple S-vacancy defects. The results open the window to large-scale near QMC quality simulations with large numbers of atoms and/or molecular dynamics configurations which would not be possible by a direct brute-force application of QMC methods.

cond-mat.mtrl-sci↗