arXiv subjects

Jose L. Lado

Publications and source records attributed to Jose L. Lado.

At least 37 records · Page 2Linked to original sources

arXiv2026

Intrinsic i-wave altermagnetism in 2D graphene superlattices

Altermagnets feature unconventional magnetism due to their momentum-dependent spin splitting purely driven by magnetic order, for which a variety of transition-metal-based d-wave altermagnets have been proposed. However, carbon-based altermagnets in graphene structures remain elusive, even though magnetism in graphene nanostructures has been widely demonstrated. Here, we establish a symmetry-guided design principle to engineer i-wave altermagnets in graphene antidot superlattices and demonstrate the emergence of altermagnetic states in specific monolayer and bilayer graphene superlattices. By combining first principles methods and atomistic tight binding models, we show the appearance of an interaction-induced i-wave altermagnetic splitting, stemming from the intrinsic magnetic instability of 2D graphene antidot superlattices. Our work establishes a strategy to engineer i-wave altermagnetism in a graphene platform, putting forward a carbon-based platform for altermagnetic spintronics.

cond-mat.mtrl-sci↗

arXiv2026

Hamiltonian learning for spin-spiral moiré magnets from electronic magnetotransport

Two-dimensional noncollinear magnetic states, such as spin-spiral magnets, offer an excellent platform for investigating fundamental phenomena, with potential for advancing stray-field-free spintronics. However, detection and characterization of noncollinear magnetic states in two-dimensional systems remain challenging, motivating the development of alternative probing methods. Here, we present a methodology for extracting the spin-spiral $\mathbf{q}$ vector from lateral electronic transport measurements. Our approach leverages the magnetic field and bias dependence of the conductance to train a supervised machine learning algorithm, which enables us to extract the $\mathbf{q}$ vectors of arbitrary spin-spiral magnets. We demonstrate that this methodology is robust to the presence of impurities in the system and noise in the conductance data. Our findings show that the conductance pattern reveals a complex dependence on the $\mathbf{q}$ vector of the spin spiral, providing a new strategy to learn magnetic structures directly from transport experiments.

cond-mat.mes-hall↗

arXiv2026

Atomically Reconfigurable Single-Molecule Optoelectronics

Deterministic control of excitonic properties is key to advancing nanoscale optoelectronic and quantum technologies and to understanding diverse physical, optical, chemical, and biological phenomena. At the molecular scale, these properties can be tuned through chemical modification, local-environment influence or charge-state manipulation. Yet, direct control of a molecule's transition dipole moment and its resulting light emission via atomic-scale structural modification has remained elusive. Here, using scanning tunnelling microscopy-induced luminescence, we show that a single structural parameter-the vertical displacement of the central metal atom in a planar phthalocyanine molecule on a decoupling layer-enables active tuning of the transition dipole, allowing either suppression or enhancement of emission. Exploiting this control, we realized a tunable homodimer switchable among three optical states: non-emissive, single-molecule-like emissive, and coupled states exhibiting subradiant and superradiant modes, directly revealing intermolecular dipole-dipole coupling. We further demonstrate a heterodimer in which resonant energy transfer can be turned on or off simply by controlling the acceptor's transition dipole moment. These findings not only establish atomic-scale displacement as a general strategy for optical molecular switching, but also demonstrate the reconfigurable engineering of excitonic interactions within molecular assemblies.

cond-mat.mtrl-sci↗

arXiv2026

Molecular Hamiltonian learning from setpoint-dependent scanning tunneling spectroscopy

Molecular quantum magnets adsorbed on surfaces exhibit rich spin and orbital excitations that can be probed by scanning tunneling microscopy with inelastic electron tunneling spectroscopy (STM-IETS). However, the quantitative extraction of the underlying multiorbital Hamiltonian from experimental spectra remains a fundamental challenge. Here, we introduce molecular Hamiltonian learning, a machine learning strategy that infers the microscopic Hamiltonian parameters of a single adsorbed molecule directly from the setpoint-dependence of STM-IETS data. The method leverages the systematic evolution of spectral features as the STM tip tunes the local electrostatic environment for different tip-sample distances. We demonstrate this approach on iron phthalocyanine on ferroelectric SnTe, training our algorithm on theory spectra from a realistic multiorbital model, including spin-orbit coupling, electrostatic interactions, local crystal field, and substrate effects. The algorithm, trained solely on theoretical many-body simulations, allows reconstructing Hamiltonian parameters directly from experimental spectra. Our manuscript establishes a flexible and automated strategy for Hamiltonian reconstruction from STM-IETS, transforming setpoint-dependent spectroscopy into quantitative characterization of quantum materials at the atomic scale.

cond-mat.mes-hall↗

arXiv2026

Coupling of Klein-Andreev Resonant States in Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$-graphene-Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ Devices

Quantum devices require coherent coupling over macroscopic distances. Recently, resonances due to Klein tunneling and Andreev reflection states (KARS) have been observed in a naturally occurring p-n junction at the interface between Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ (BSCCO), a high-Tc superconductor (HTS), and graphene. The resonances appear as conductance oscillations with gating. Here, we show coupling between the KARS in BSCCO-graphene-BSCCO devices of varying separation (L). The coupling is evidenced by a power-law decay of resonance period as L increases from tens of nanometers to single microns. These results demonstrate the long-distance coupling of KARS cavities in graphene-HTS junctions. The length dependence seen in experiments is supported by single-particle spectral functions which show KARS are coupled by transport modes in graphene. The strong coupling between KARS in BSCCO-graphene-BSCCO junctions showcases the novelty of HTS-graphene junctions for quantum circuits and unconventional Josephson junctions.

cond-mat.supr-con↗

arXiv2025

Self-consistent tensor network method for correlated super-moiré matter beyond one billion sites

Moiré and super-moiré materials provide exceptional platforms to engineer exotic correlated quantum matter. The vast number of sites required to model moiré systems in real space remains a formidable challenge due to the immense computational resources required. Super-moiré materials push this requirement to the limit, where millions or even billions of sites need to be considered, a requirement beyond the capabilities of conventional methods for interacting systems. Here, we establish a methodology that allows solving correlated states in systems reaching a billion sites, that exploits tensor-network representations of real-space Hamiltonians and self-consistent real-space mean-field equations. Our method combines a tensor-network kernel polynomial method with quantics tensor cross interpolation algorithm, enabling us to solve exponentially large models, including those whose single particle Hamiltonian is too large to be stored explicitly. We demonstrate our methodology with super-moiré systems featuring spatially modulated hoppings, many-body interactions and domain walls, showing that it allows access to self-consistent symmetry broken states and spectral functions of real-space models reaching a billion sites. Our methodology provides a strategy to solve exceptionally large interacting problems, providing a widely applicable strategy to compute correlated super-moiré quantum matter.

cond-mat.str-el↗

arXiv2025

Atomic-scale probe of molecular magneto-electric coupling

Van der Waals heterostructures are a core tool in quantum material design. The recent addition of monolayer ferroelectrics expands the possibilities of designer materials. Ferroelectric domains can be manipulated using electric fields, thus opening a route for external control over material properties. In this paper we explore the possibility of engineering magneto-electric coupling in ferroelectric heterostructures by studying the interface of bilayer SnTe with iron phthalocyanine molecules as a model system. The molecules act as sensor spins, allowing us to sample the magneto-electric coupling with nanometer precision through scanning tunneling microscopy. Our measurements uncover a structural, and therefore material-independent and intrinsic, mechanism to couple electric and magnetic degrees of freedom at the nanoscale.

cond-mat.mes-hall↗

arXiv2025

Transfer learning of many-body electronic correlation entropy from local measurements

The characterization of quantum correlations in many-body systems is instrumental to understanding the nature of emergent phenomena in quantum materials. The correlation entropy serves as a key metric for assessing the complexity of a quantum many-body state in interacting electronic systems. However, its determination requires the measurement of all single-particle correlators across a macroscopic sample, which can be impractical. Machine learning methods have been shown to allow learning the correlation entropy from a reduced set of measurements, yet these methods assume that the targeted system is contained in the set of training Hamiltonians. Here we show that a transfer learning strategy enables correlation entropy learning from a reduced set of measurements in families of Hamiltonians never considered in the training set. We demonstrate this transfer learning methodology in a wide variety of interacting models including local and non-local attractive and repulsive many-body interactions, long-range hopping, doping, magnetic field, and spin-orbit coupling. Furthermore, we show how this transfer learning methodology allows detecting quantum many-body phases never observed during their training set without prior knowledge about them. Our results demonstrate that correlation entropy learning can be potentially performed experimentally without requiring training in the experimentally realized Hamiltonian.

cond-mat.str-el↗

arXiv2025

Moiré modulated quantum spin liquid candidate 1T-TaSe$_2$

Quantum spin liquids are quantum phases of matter featuring collectively entangled states and emergent fractional many-body excitations. While methods exist to probe three-dimensional quantum spin liquids experimentally, these techniques lack the sensitivity to probe two-dimensional quantum spin liquids. This seriously hampers the study of potential monolayer quantum spin liquid candidates such as $α$-RuCl$_3$ and 1T-TaSe$_2$. Scanning tunneling microscopy (STM) and spectroscopy (STS) have recently been suggested as promising probes of the quantum spin liquid state, as they can access the spinon spectrum through inelastic tunneling spectroscopy (IETS). In this work, we employ this approach on the quantum spin liquid candidate material 1T-TaSe$_2$ and directly measure its low-energy inelastic excitations. We observe the emergence of a $\sqrt{3}\times\sqrt{3}$ reconstruction driven by the substrate, equivalent spectroscopy across all spin sites and coexistence of zero and finite energy excitations. We show that these observations are consistent with a modulated $\sqrt{3}\times\sqrt{3}$ spin liquid ground state. Our results demonstrate that IETS provides a powerful route to obtain atomic-scale insight into the magnetic excitations of two-dimensional materials, allowing to explore the effects of moiré modulations on potential quantum liquid phases.

cond-mat.str-el↗

arXiv2025

Observation of tunable chiral spin textures with nonlinear optics

Chiral spin textures, such as spin spirals and skyrmions, are key to advancing spintronics by enabling ultrathin, energy-efficient memory, and high-density data storage and processing. However, their realization remains hindered by the scarcity of suitable host materials and the formidable experimental challenges associated with the characterization of these intricate chiral magnetic states. Here, we report the observation of tunable chiral magnetic textures in van der Waals magnet CrPS$_4$ with nonlinear optics. These tunable textures exhibit strong chiral third-order nonlinear optical responses, driven by interlayer and intralayer spin couplings under varying magnetic fields and temperatures. These pronounced chiral nonlinear optical responses highlight the potency and high sensitivity of the nonlinear optical readout for probing non-collinear magnetic orders. Moreover, our findings position van der Waals magnets and their heterostructures as an exceptional platform for reconfigurable spin-photonics and spintronics, unifying optical, electrical, and magnetic properties through unique intralayer and interlayer spin coupling properties and effective spin interaction between photons and electrons.

cond-mat.mes-hall↗

arXiv2025

Tensor network method for real-space topology in quasicrystal Chern mosaics

Computing topological invariants in two-dimensional quasicrystals and super-moire matter is a remarkable open challenge, due to the absence of translational symmetry and the colossal number of sites inherent to these systems. Here, we establish a method to compute local topological invariants of exceptionally large systems using tensor networks, enabling the computation of invariants for Hamiltonians with hundreds of millions of sites, several orders of magnitude above the capabilities of conventional methodologies. Our approach leverages a tensor-network representation of the density matrix using a Chebyshev tensor network algorithm, enabling large-scale calculations of topological markers in quasicrystalline and moire systems. We demonstrate our methodology with two-dimensional quasicrystals featuring $C_8$ and $C_{10}$ rotational symmetries and mosaics of Chern phases. Our work establishes a powerful method to compute topological phases in exceptionally large-scale topological systems, providing the required tool to rationalize generic supe-moire and quasicrystalline topological matter.

cond-mat.str-el↗

arXiv2025

Hamiltonian Learning of Triplon Excitations in an Artificial Nanoscale Molecular Quantum Magnet

Extracting the Hamiltonian parameters of nanoscale quantum magnets from experimental measurements is a significant challenge in quantum matter. Here we establish a machine learning strategy to extract the parameters of a spin Hamiltonian from inelastic spectroscopy with scanning tunneling microscopy, and we demonstrate this methodology experimentally with an artificial nanoscale molecular magnet based on cobalt phthalocyanine (CoPC) molecules on NbSe$_2$. We show that this technique allows us to extract the Hamiltonian parameters of a quantum magnet from the differential conductance, including the substrate-induced spatial variation of the exchange couplings. Our methodology leverages a machine learning algorithm trained on exact quantum many-body simulations with tensor networks of finite quantum magnets, leading to a methodology that predicts the Hamiltonian parameters of CoPC quantum magnets of arbitrary size. Our results demonstrate how quantum many-body methods and machine learning enable us to learn a microscopic description of nanoscale quantum many-body systems with scanning tunneling spectroscopy.

cond-mat.mes-hall↗

arXiv2025

Electrical probe of spin-spiral order in quantum spin Hall/spin-spiral magnet van der Waals heterostructures

Two-dimensional spin-spiral magnets provide promising building blocks for van der Waals heterostructures due to their tunable spin textures and potential for novel functionalities for quantum devices. However, due to its vanishing magnetization and two-dimensional nature, it is challenging to detect the existence of its noncollinear magnetization. Here, we show that a van der Waals junction based on a spin-spiral magnet and a quantum spin Hall insulator enables obtaining signatures of noncollinear magnetization directly from electrical measurements. Our strategy exploits the sensitivity of helical states to local breaking of time-reversal symmetry, potentially enabling the detection of local magnetic orders even in the absence of net magnetization. We show that the combination of spin-spiral order and nonmagnetic disorder gives rise to scattering in the helical channels that can be directly associated with the spiral exchange coupling and residual nonmagnetic disorder strength. Our results show how electrical transport measurement may offer a way to detect spin-spiral magnets by leveraging helical states in van der Waals heterostructures.

cond-mat.mes-hall↗

arXiv2025

Solving the Gross-Pitaevskii equation on multiple different scales using the quantics tensor train representation

Solving partial differential equations of highly featured problems represents a formidable challenge, where reaching high precision across multiple length scales can require a prohibitive amount of computer memory or computing time. However, the solutions to physics problems typically have structures operating on different length scales, and as a result exhibit a high degree of compressibility. Here, we use the quantics tensor train representation to build a solver for the time-dependent Gross-Pitaevskii equation. We demonstrate that the quantics approach generalizes well to the presence of the non-linear term in the equation. We show that we can resolve phenomena across length scales separated by seven orders of magnitude in one dimension within one hour on a single core in a laptop, greatly surpassing the capabilities of more naive methods. We illustrate our methodology with various modulated optical trap potentials presenting features at vastly different length scales, including solutions to the Gross-Pitaevskii equation on two-dimensional grids above a trillion points ($2^{20} \times 2^{20}$). This quantum-inspired methodology can be readily extended to other partial differential equations combining spatial and temporal evolutions, providing a powerful method to solve highly featured differential equations at unprecedented length scales.

quant-ph↗

arXiv2025

Hamiltonian-learning quantum magnets with non-local impurity tomography

Impurities in quantum materials have provided successful strategies for learning properties of complex states, ranging from unconventional superconductors to topological insulators. In quantum magnetism, inferring the Hamiltonian of an engineered system becomes a challenging open problem in the presence of complex interactions. Here we show how a supervised machine-learning technique can be used to infer Hamiltonian parameters from atomically engineered quantum magnets by inferring fluctuations of the ground states due to the presence of impurities. We demonstrate our methodology both with a fermionic model with spin-orbit coupling, as well as with many-body spin models with long-range exchange and anisotropic exchange interactions. We show that our approach enables performing Hamiltonian extraction in the presence of significant noise, providing a strategy to perform Hamiltonian learning with experimental observables in atomic-scale quantum magnets. Our results establish a strategy to perform Hamiltonian learning by exploiting the impact of impurities in complex quantum many-body states.

cond-mat.mes-hall↗

arXiv2025

Control of Andreev Reflection via a Single-Molecule Orbital

Charge transport across a single-molecule junction fabricated from a normal-metal tip, a phthalocyanine, and a conventional superconductor in a scanning tunneling microscope is explored as a function of the gradually closed vacuum gap. The phthalocyanine (2H-Pc) molecule and its pyrrolichydrogen-abstracted derivative (Pc) exhibit vastly different behavior. Andreev reflection across the 2H-Pc contact exhibits a temporary enhancement that diminishes with increasing conductance. The hybridization of 2H-Pc with the tip at contact formation gives rise to a Kondo-screened molecular magnetic moment. In contrast, the single-Pc junction lacks Andreev reflection in the same conductance range. Spectroscopies and supporting nonequilibrium Green function calculations highlight the importance of a molecular orbital close to the Fermi energy for Andreev reflection.

cond-mat.mes-hall↗

arXiv2025

Multiferroic nematic d-wave altermagnetism driven by orbital-order on the honeycomb lattice

Altermagnets provide promising platforms for unconventional magnetism, whose controllability would enable a whole new generation of spintronic devices. While a variety of bulk altermagnets have been discovered, altermagnetism in two-dimensional van der Waals materials has remained elusive. Here we demonstrate that the strained honeycomb monolayer VCl$_{3}$ is an orbital-order-driven ferroelectric altermagnet, exhibiting a significant and switchable spin-splitting. By using low-energy Hamiltonian and first-principles methods in combination with symmetry analysis, we reveal a unique anti-ferro-orbital-antiferromagnetic phase characterized by a 2D nematic $d$-wave altermagnetic spin splitting, tightly coupled with an orbital-ordered induced ferroelectric polarization. Finally, through symmetry mode analysis, we investigate how structural distortions favor the intricate interplay between orbital, altermagnetic, and ferroelectric degrees of freedom. Our study identifies VCl$_3$ as a prototypical 2D orbital-order-driven multiferroic altermagnet on the honeycomb lattice, establishing a van der Waals monolayer featuring altermagnetic ferroelectricity.

cond-mat.mtrl-sci↗

arXiv2025

Lee-Yang formalism for phase transitions of interacting fermions using tensor networks

Predicting the phase diagram of interacting quantum many-body systems is a challenging problem in condensed matter physics. Strong interactions and correlation effects may lead to exotic states of matter, such as quantum spin liquids and unconventional superconductors, that often compete with other symmetry broken states including ordered magnets and charge density waves. Here, we put forward a formalism for determining the phase diagram of fermionic systems that combines recent progress in the field of Lee-Yang theory of phase transitions with many-body tensor-network methods. Using this strategy, we map out the phase diagram of a fermionic chain, where charge density waves form owing to strong repulsion. Specifically, from the high cumulants of the order parameter, we extract the dominant zeros of the moment-generating function in chains of finite size. By extrapolating their positions to the thermodynamic limit, we determine the boundaries between competing phases. Our formalism provides a strategy for determining critical points in fermionic systems, and it is based on fluctuations of the order parameter, which are measurable quantities.

cond-mat.str-el↗