arXiv subjects

Vincenzo Lordi

Publications and source records attributed to Vincenzo Lordi.

At least 19 recordsLinked to original sources

arXiv2026

Extracting Atomic Environments for Machine Learning Interatomic Potentials

In order to appropriately capture large-scale material features and emergent phenomena via atomistic simulations, such as Molecular Dynamics (MD), the system scale can range up to hundreds of millions of atoms. However, the force-field models that drive those simulations are generally trained with Density Functional Theory (DFT) reference data, limited to relatively small configurations on the order of 100s or 1000s of atoms. To compute DFT forces on atoms in regions of interest, for example for active-learning or on-the-fly training of interatomic potentials, one needs to extract a small set of atoms from the larger simulation box, and typically work with periodic boundary conditions for DFT. However, methods to select the shape and size of this extracted set of atoms, as well as to generate a potentially necessary passivating envelope, have not been systematically analyzed. In this work, we benchmark several techniques, including a generative diffusion-based artificial intelligence (AI) approach, for extracting atomic environments from large, bulk configurations and embedding them into smaller configurations suitable for DFT calculations with periodic boundary conditions. We test with a diverse set of material systems, which includes amorphous $\mathrm{SiO_2}$, Ta with screw dislocations, and molten C. We demonstrated a notably simple procedure, a method we refer to as deletions, yields superior performance over an array of alternative extraction methods.

cond-mat.mtrl-sci↗

arXiv2026

Inverse design of bespoke interatomic potentials via active learning by information-matching

Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selection of training data, quantified uncertainty, and model expressiveness. Active learning (AL) provides a principled framework for constructing efficient and accurate IPs, yet most strategies reduce parameter uncertainty without explicitly accounting for the specific material properties being predicted. The information-matching (IM) approach addresses this limitation by requiring that the selected training data provide at least as much parameter space information as needed to achieve prescribed uncertainty targets for selected quantities of interest (QoIs). Here, we apply IM to develop bespoke IPs specifically tailored for predicting plastic strength in metals. Due to the high computational cost of simulating plastic strength, we employ an indirect IM strategy that targets inexpensive intermediate QoIs that correlate with strength. The IM method enables precise parameter constraints with minimal training data, yielding precise predictions for both the intermediate QoIs and plastic strength. Yet, model error remains a key limitation, and a post hoc uncertainty inflation correction provides a viable means to mitigate this limitation. These findings illustrate both the promise and limits of uncertainty-aware AL for predicting complex material properties.

cond-mat.mtrl-sci↗

arXiv2026

Aluminum-Based Superconducting Tunnel Junction Sensors for Nuclear Recoil Spectroscopy

The BeEST experiment is searching for sub-MeV sterile neutrinos by measuring nuclear recoil energies from the decay of $^7$Be implanted into superconducting tunnel junction (STJ) sensors. The recoil spectra are affected by interactions between the radioactive implants and the sensor materials. We are therefore developing aluminum-based STJs (Al-STJs) as an alternative to existing tantalum devices (Ta-STJs) to investigate how to separate material effects in the recoil spectrum from potential signatures of physics beyond the Standard Model. Three iterations of Al-STJs were fabricated. The first had electrode thicknesses similar to existing Ta-STJs. They had low responsivity and reduced resolution, but were used successfully to measure $^7$Be nuclear recoil spectra. The second iteration had STJs suspended on thin SiN membranes by backside etching. These devices had low leakage current, but also low yield. The final iteration was not backside etched, and the Al-STJs had thinner electrodes and thinner tunnel barriers to increase signal amplitudes. These devices achieved 2.96 eV FWHM energy resolution at 50 eV using a pulsed 355 nm (~3.5 eV) laser. These results establish Al-STJs as viable detectors for systematic material studies in the BeEST experiment.

physics.ins-det↗

arXiv2026

A probabilistic framework for crystal structure denoising, phase classification, and order parameters

Atomistic simulations generate large volumes of noisy structural data, yet extracting phase labels and continuous order parameters (OPs) in a robust and general manner remains challenging. Existing tools are often specialized to a limited set of prototypes and split thermal-noise removal, phase classification, and OP construction into separate steps. Here we present a unified probabilistic framework for analyzing noisy atomic configurations with respect to known crystal prototypes. The model predicts per-atom, per-prototype logits and aggregates them into a scalar log-probability (logP) landscape over atomic coordinates. Its gradient defines a conservative denoising field, while the logits provide local phase labels, prototype-resolved OPs, and ambiguity measures through logit margins. We train on AFLOW-mapped crystalline structures from the Materials Project with synthetic positional and elastic perturbations, then test extrapolation to stronger noise, finite-temperature disorder, point defects, water--ice coexistence, binary polymorphs, and shock-compressed Ti. A single differentiable scalar model recovers prototype identity after denoising, tracks smooth transformations such as Bain and Burgers paths, and exposes low-confidence regions near defects and phase boundaries. This provides an integrated and extensible tool for analyzing complex atomistic simulations.

cond-mat.mtrl-sci↗

arXiv2026

Probing Coordination Environments in Buried Oxides of Aluminum Josephson Junctions by Resonant X-ray Reflectivity

Decoherence remains a critical obstacle to achieving high-fidelity, scalable superconducting qubits, with the tunnel barrier of Josephson junctions a key source of loss. Here we apply resonant X-ray reflectivity to non-destructively probe the electronic structure of buried layers in Al/AlO$_x$/Al Josephson junctions. At the Al $K$-edge, energy-dependent modulations in the reflectivity maps enable Kramers-Kronig-constrained extraction of the layer-resolved atomic scattering factors. The analysis reveals that the barrier coordination evolves from more tetrahedral toward predominantly octahedral character with increasing oxidation pressure. At the interfaces, the lower metal-oxide boundary is comparatively under-coordinated and disordered relative to the upper interface. Comparison with simulated X-ray absorption spectra identifies the dominant coordination motifs within the oxide and its interfaces, providing depth-resolved structural insight that constrains microscopic models of two-level system formation. These results link growth conditions, local coordination environments, and junction electronic properties, demonstrating resonant X-ray reflectivity as a powerful tool for probing the microscopic materials properties of Josephson junctions and providing a materials-level framework for mitigating decoherence in superconducting qubits.

cond-mat.mtrl-sci↗

arXiv2026

Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis

An adaptive physics-inspired model design strategy for machine-learning interatomic potentials (MLIPs) is proposed. This strategy relies on iterative reconfigurations of composite models from single-term models, followed by a unified training procedure. A model evaluation method based on the Fisher information matrix (FIM) and multiple-property error metrics is also proposed to guide the model reconfiguration and hyperparameter optimization. By combining the reconfiguration and the evaluation subroutines, we provide an adaptive MLIP design strategy that balances flexibility and extensibility. In a case study of designing models against a structurally diverse niobium dataset, we managed to obtain an optimal model configuration with 75 parameters generated by our framework that achieved a force RMSE of 0.172 eV/Å and an energy RMSE of 0.013 eV/atom.

cond-mat.mtrl-sci↗

arXiv2026

Field-Dependent Qubit Flux Noise Simulated from Materials-Specific Disordered Exchange Interactions Between Paramagnetic Adsorbates

Superconducting quantum devices, from qubits and magnetometers to dark matter detectors, are influenced by magnetic flux noise originating from paramagnetic surface defects and impurities. These spin systems can feature complex dynamics, including a Berezinskii-Kosterlitz-Thouless transition, that depend on the lattice, interactions, external fields, and disorder. However, the disorder included in typical models is not materials-specific, diminishing the ability to accurately capture measured flux noise phenomena. We present a first principles-based simulation of a spin lattice consisting of paramagnetic O$_2$ molecules on an Al$_2$O$_3$ surface, a likely flux noise source in superconducting qubits, to elucidate opportunities to mitigate flux noise. We simulate an ensemble of surface adsorbates with disordered orientations and calculate the orientation-dependent exchange couplings using density functional theory. Thus, our spin simulation has no free parameters or assumed functional form of the disorder, and captures correlation in the defect landscape that would appear in real systems. We calculate a range of exchange interactions between electron pairs, with the smallest values, 0.016 meV and -0.023 meV, being in the range required to act as a two-level system and couple to GHz resonators. We calculate the flux noise frequency, temperature, and applied external magnetic field dependence, as well as the susceptibility-flux noise cross-correlation. Calculated trends agree with experiment, demonstrating that a surface harboring paramagnetic adsorbates arranged with materials-specific disorder and interactions captures the various properties of magnetic flux noise observed in superconducting circuits. In addition, we find that an external electric field can tune the spin-spin interaction strength and reduce magnetic flux noise.

cond-mat.mes-hall↗

arXiv2026

Shake-up and shake-off spectra in the electron capture decay of atomic $^7$Be

The most stringent laboratory-based experimental limits on the existence of sub-MeV sterile neutrinos are currently set by decay spectroscopy of radioactive $^7$Be embedded into superconducting sensors. The systematic uncertainties are dominated by the modeling of the electron shake-up and shake-off spectra that are not based on state-of-the-art atomic theory and do not include electron correlations or relativistic effects. We have used the multiconfiguration Dirac-Fock formalism to obtain correlated wavefunctions ab initio and compute all single and double shake processes in the electron capture decay of atomic $^7$Be. The simulations can explain some but not all of the observed spectral features, likely because the wave functions are modified by the Ta sensor material that the $^7$Be is embedded into. The new models also show that the L/K electron capture ratio of $^7$Be in Ta has previously been slightly underestimated revising the previous value of 0.070(7) to a new value of 0.0756(20).

physics.atom-ph↗

arXiv2026

An information-matching approach to optimal experimental design and active learning

The efficacy of mathematical models heavily depends on the quality of the training data, yet collecting sufficient data is often expensive and challenging. Many modeling applications require inferring parameters only as a means to predict other quantities of interest (QoI). Because models often contain many unidentifiable (sloppy) parameters, QoIs often depend on a relatively small number of parameter combinations. Therefore, we introduce an information-matching criterion based on the Fisher Information Matrix to select the most informative training data from a candidate pool. This method ensures that the selected data contain sufficient information to learn only those parameters that are needed to constrain downstream QoIs. It is formulated as a convex optimization problem, making it scalable to large models and datasets. We demonstrate the effectiveness of this approach across various modeling problems in diverse scientific fields, including power systems and underwater acoustics. Finally, we use information-matching as a query function within an Active Learning loop for material science applications. In all these applications, we find that a relatively small set of optimal training data can provide the necessary information for achieving precise predictions. These results are encouraging for diverse future applications, particularly active learning in large machine learning models.

cs.LG↗

arXiv2025

A scanning probe microscopy approach for identifying defects in aluminum oxide

The coherence of quantum dot qubits fabricated in semiconductors is often limited by charge noise from defects in gate dielectrics, which are material- and process-dependent. Characterizing these defects is an important step towards reducing their impact and improving qubit coherence. The identification of individual defects requires atomic-scale spatial resolution, however, and sufficient spectral sensitivity to determine their electronic structure. Electrostatic force microscopy (EFM) provides highly resolved maps of the surface potential of dielectrics, and importantly, is also sensitive to single-electron charging processes that reflect the spectral structure of underlying defects. In this work, we use cryogenic EFM to characterize aluminum oxide grown by atomic layer deposition (ALD) on bulk silicon. These measurements reveal defects close to the surface that exchange electrons with the EFM tip as they transition through different charge states. Detailed electrostatic modeling opens the door to powerful techniques for mapping tip-backgate charging voltages onto defect transition energies, allowing defects such as aluminum vacancies, and carbon, oxygen, or hydrogen impurities to be identified, by comparing to density functional theory (DFT). These results point towards EFM as a powerful tool for exploring defect structures in solid-state qubits.

cond-mat.mes-hall↗

arXiv2025

Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets, and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

cs.LG↗

arXiv2025

Unsupervised Atomic Data Mining via Multi-Kernel Graph Autoencoders for Machine Learning Force Fields

Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions of the potential energy surface. Furthermore, these regions can be difficult to identify and isolate from each other or may not align well with human intuition, making it challenging to systematically remove bias in the dataset. While traditional clustering and pruning (down-sampling) approaches can be useful for this, they can often lead to information loss or a failure to properly identify distinct regions of the potential energy surface due to difficulties associated with the high dimensionality of atomic descriptors. In this work, we introduce the Multi-kernel Edge Attention-based Graph Autoencoder (MEAGraph) model, an unsupervised approach for analyzing atomic datasets. MEAGraph combines multiple linear kernel transformations with attention-based message passing to capture geometric sensitivity and enable effective dataset pruning without relying on labels or extensive training. Demonstrated applications on niobium, tantalum, and iron datasets show that MEAGraph efficiently groups similar atomic environments, allowing for the use of basic pruning techniques for removing sampling bias. This approach provides an effective method for representation learning and clustering that can be used for data analysis, outlier detection, and dataset optimization.

cs.LG↗

arXiv2025

Towards MatCore: A Unified Metadata Standard for Materials Science

The materials science community seeks to support the FAIR principles for computational simulation research. The MatCore Project was recently launched to address this need, with the goal of developing an overall metadata framework and accompanying guidelines. This paper reports on the MatCore goals and overall progress. Historical background context is provided, including a review of the principles underlying successful core metadata standards. The paper also presents selected MatCore examples and discusses future plans.

cond-mat.mtrl-sci↗

arXiv2025

Signal processing and spectral modeling for the BeEST experiment

The Beryllium Electron capture in Superconducting Tunnel junctions (BeEST) experiment searches for evidence of heavy neutrino mass eigenstates in the nuclear electron capture decay of $^7$Be by precisely measuring the recoil energy of the $^7$Li daughter. In Phase-III, the BeEST experiment has been scaled from a single superconducting tunnel junction (STJ) sensor to a 36-pixel array to increase sensitivity and mitigate gamma-induced backgrounds. Phase-III also uses a new continuous data acquisition system that greatly increases the flexibility for signal processing and data cleaning. We have developed procedures for signal processing and spectral fitting that are sufficiently robust to be automated for large data sets. This article presents the optimized procedures before unblinding the majority of the Phase-III data set to search for physics beyond the standard model.

physics.ins-det↗

arXiv2025

Growth and Structure of alpha-Ta films for Quantum Circuit Integration

Tantalum films incorporated into superconducting circuits have exhibited low surface losses, resulting in long-lived qubit states. Remaining loss pathways originate in microscopic defects which manifest as two level systems (TLS) at low temperature. These defects limit performance, so careful attention to tantalum film structures is critical for optimal use in quantum devices. In this work, we investigate the growth of tantalum using magnetron sputtering on sapphire, Si, and photoresist substrates. In the case of sapphire, we present procedures for growth of fully-oriented films with alpha-Ta [1 1 1] // Al2O3 [0 0 0 1] and alpha-Ta [1 -1 0] // Al2O3 [1 0 -1 0] orientational relationships, and having residual resistivity ratios (RRR) ~ 60 for 220 nm thick films. On Si, we find a complex grain texturing with Ta [1 1 0] normal to the substrate and RRR ~ 30. We further demonstrate airbridge fabrication using Nb to nucleate alpha-Ta on photoresist surfaces. For the films on sapphire, resonators show TLS-limited quality factors of 1.3 +/- 0.3 x 10^6 at 10 mK (for waveguide gap and conductor widths of 3 microns and 6 microns, respectively). Structural characterization using scanning electron microscopy, X-ray diffraction, low temperature transport, secondary ion mass spectrometry, and transmission electron microscopy reveal the dependence of residual impurities and screw dislocation density on processing conditions. The results provide practical insights for fabrication of advanced superconducting devices including qubit arrays, and guide future work on crystallographically deterministic qubit fabrication.

cond-mat.supr-con↗

arXiv2024

Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory

An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets. However, atomistic ML often relies on unsupervised learning or model predictions to analyze information contents from simulation or training data. Here, we introduce a theoretical framework that provides a rigorous, model-free tool to quantify information contents in atomistic simulations. We demonstrate that the information entropy of a distribution of atom-centered environments explains known heuristics in ML potential developments, from training set sizes to dataset optimality. Using this tool, we propose a model-free UQ method that reliably predicts epistemic uncertainty and detects out-of-distribution samples, including rare events in systems such as nucleation. This method provides a general tool for data-driven atomistic modeling and combines efforts in ML, simulations, and physical explainability.

cond-mat.mtrl-sci↗

arXiv2024

Sub-nanometer-thick native sp2 carbon on oxidized diamond surfaces

Oxygen-terminated diamond has a wide breadth of applications, which include stabilizing near-surface color centers, semiconductor devices, and biological sensors. Despite the vast literature on characterizing functionalization groups on diamond, the chemical composition on the shallowest portion of the surface (< 1 nm) is challenging to probe with conventional techniques like XPS and FTIR. In this work, we demonstrate the use of angle-resolved XPS to probe the first ten nanometers of (100) single-crystalline diamond, showing the changes of the oxygen functional groups and the allotropes of carbon with respect to depth. With the use of consistent peak-fitting methods, the peak identities and relative peak binding energies were identified for sp2 carbon, ether, hydroxyl, carbonyl, and C-H groups. For the oxygen-terminated sample, we also quantified the thickness of the sp2 carbon layer situated on top of the bulk sp3 diamond bonded carbon to be 0.4 $\pm$ 0.1 nm, based on the analysis of the Auger electron spectra and D-parameter calculations. These results indicate that the majority of the oxygen is bonded to the sp2 carbon layer on the diamond, and not directly on the sp3 diamond bonded carbon.

cond-mat.mtrl-sci↗

arXiv2024

Grand canonical generative diffusion model for crystalline phases and grain boundaries

The diffusion model has emerged as a powerful tool for generating atomic structures for materials science. This work calls attention to the deficiency of current particle-based diffusion models, which represent atoms as a point cloud, in generating even the simplest ordered crystalline structures. The problem is attributed to particles being trapped in local minima during the score-driven simulated annealing of the diffusion process, similar to the physical process of force-driven simulated annealing. We develop a solution, the grand canonical diffusion model, which adopts an alternative voxel-based representation with continuous rather than fixed number of particles. The method is applied towards generation of several common crystalline phases as well as the technologically important and challenging problem of grain boundary structures.

cond-mat.mtrl-sci↗