arXiv subjects

Francesco Laviano

Publications and source records attributed to Francesco Laviano.

6 recordsLinked to original sources

arXiv2026

Machine-Learned Interatomic Potentials for Structural and Defect Properties of YBa$_2$Cu$_3$O$_{7-δ}$

High-Temperature Superconductors (HTS) such as YBa2Cu3O7-delta (YBCO) are essential for next-generation Tokamak fusion reactors, where Rare-Earth Barium Copper Oxides (REBCO) form the functional layers in HTS magnets. Because YBCO's superconductivity depends strongly on oxygen stoichiometry and defect structure, atomistic simulations can provide crucial insight into radiation-damage mechanisms and pathways to maintain material performance. In this work, we develop and benchmark four Machine-Learned Interatomic Potentials (MLPs) for YBCO: the Atomic Cluster Expansion (ACE), the Message-Passing Atomic Cluster Expansion (MACE), the Gaussian Approximation Potential (GAP), and the Tabulated Gaussian Approximation Potential (tabGAP), trained on an extensive Density Functional Theory (DFT) database explicitly designed to include irradiation-damaged-like configurations. The resulting models achieve DFT-level accuracy across a wide range of atomic environments, faithfully capturing the interatomic forces relevant to radiation damage processes. Among the tested models, MACE delivers the highest accuracy, although at greater computational cost, while ACE and tabGAP provide an excellent balance between efficiency and fidelity. These machine-learned potentials establish a robust foundation for large-scale molecular dynamics simulations of radiation-induced defect evolution in complex superconducting materials

cond-mat.supr-con↗

arXiv2026

Insights Into Radiation Damage in YBa$_2$Cu$_3$O$_{7-δ}$ From Machine-Learned Interatomic Potentials

Accurate prediction of radiation damage in YBa$_2$Cu$3$O${7-δ}$ (YBCO) is essential for assessing the performance of high-temperature superconducting (HTS) tapes in compact fusion reactors. Existing empirical interatomic potentials have been used to model radiation damage in stoichiometric YBCO, but fail to describe oxygen-deficient compositions, which are ubiquitous in industrial Rare-Earth Barium Copper Oxide conductors and strongly influence superconducting properties. In this work, we demonstrate that modern machine-learned interatomic potentials enable predictive modelling of radiation damage in YBCO across a wide range of oxygen stoichiometries, with higher fidelity than previous empirical models. We employ two recently developed approaches: an Atomic Cluster Expansion (ACE) potential and a tabulated Gaussian Approximation Potential (tabGAP). Both models accurately reproduce Density Functional Theory (DFT) energies, forces, and threshold displacement energy distributions, providing a reliable description of atomic-scale collision processes. Molecular dynamics simulations of 5 keV cascades predict enhanced peak defect production and recombination relative to a widely used empirical potential, indicating different cascade evolution. By explicitly varying oxygen deficiency, we show that total defect production depends only weakly on stoichiometry, offering insight into the robustness of radiation damage processes in oxygen-deficient YBCO. Finally, fusion-relevant 300 keV cascade simulations reveal amorphous regions with dimensions comparable to the superconducting coherence length, consistent with electron microscopy observations of neutron-irradiated HTS tapes. These results establish machine-learned interatomic potentials as efficient and predictive tools for investigating radiation damage in YBCO across relevant compositions and irradiation conditions.

cond-mat.supr-con↗

arXiv2025

Beyond dpa: an atomistic framework for a quantitative description of radiation damage in YBa2Cu3O7

Radiation damage in high-temperature cuprate superconductors represents one of the main technological challenges for their deployment in harsh environments, such as fusion reactors and accelerator facilities. Their complex crystal structure makes modeling irradiation effects in this class of materials a particularly demanding task, for which existing damage models remain inadequate. In this work, we develop an atomistic-based approach for describing primary radiation damage in YBa2Cu3O7, by coupling Molecular Dynamics and Binary Collision Approximation simulations in a way that makes them complementary. When integrated with Primary Knock-on Atom spectra obtained from Monte Carlo codes, our results establish a framework for multiscale modeling of radiation damage, enabling quantitative estimates of several damage descriptors, such as defect production, defect clustering, and the effective damaged volume for any specific irradiation conditions where collision cascades dominate. This computational approach is suitable for the prediction of irradiation effects in any complex functional oxide, with applications ranging from aerospace to nuclear fusion and high-energy physics.

cond-mat.supr-con↗

arXiv2025

The diffusion-driven orthorhombic to tetragonal transition in YBa$_2$Cu$_3$O$_7$ derived with a machine learning interatomic potential

Defects in high temperature superconductors such as YBa$_2$Cu$_3$O$_7$ (YBCO) critically influence their superconducting behavior, as they substantially degrade or even suppress superconductivity. With the renewed interest in cuprates for next-generation superconducting magnets operating in radiation-harsh environments such as fusion reactors and particle accelerators, accurate atomistic modeling of defects and their dynamics has become essential. Here, we present a general-purpose machine-learning interatomic potential for YBCO, based on the Atomic Cluster Expansion (ACE) method and trained on Density Functional Theory (DFT) data, with particular emphasis on defects and their diffusion mechanisms. The potential is validated against DFT calculations of ground-state properties, defect formation energies of oxygen Frenkel pairs and diffusion barriers for their formation. Remarkably, the potential captures the diffusion-driven orthorhombic to tetragonal transition at elevated temperatures, a transformation that is difficult to describe with empirical potentials, elucidating how the formation of oxygen Frenkel pairs in the basal plane governs this order-disorder transition. The ACE potential introduced here enables large-scale, predictive atomistic simulations of defect dynamics and transport processes in YBCO, providing a powerful tool to explore its stability, performance, and functionality under realistic operating conditions. Moreover, this work proves that machine learning interatomic potentials are suitable for studies of quaternary oxides with complex chemistry.

cond-mat.mtrl-sci↗

arXiv2020

Superconducting Transition Temperature Modulation in NbN via EDL Gating

We perform electric double-layer gating experiments on thin films of niobium nitride. Thanks to a cross-linked polymer electrolyte system of improved efficiency, we induce surface charge densities as high as $\approx 2.8 \times 10^{15}\,\mathrm{cm}^{-2}$ in the active channel of the devices. We report a reversible modulation of the superconducting transition temperature (either positive or negative depending on the sign of the gate voltage) whose magnitude and sign are incompatible with the confinement of the perturbed superconducting state to a thin surface layer, as would be expected within a naïve screening model.

cond-mat.supr-con↗

arXiv2006

Evidence of rf-driven branching of dendritic vortex avalanches in MgB2 microwave resonators

The influence of dendritic magnetic-flux penetration on the microwave response of superconducting MgB2 films is investigated by a coplanar resonator technique. The peculiar feature consists of jumps in the resonance curve, induced by vortex avalanches freezing flux inside the resonator. Due to a shaking effect, microwave currents maintain the vortex system close to a nonequilibrium state, resulting in dendrite branching. Avalanche-size distributions before and after flux-pinning tailoring by heavy-ion irradiation are fully consistent with molecular dynamics simulations reported in literature.

cond-mat.supr-con↗