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Rondinelli, James M. (ORCID:0000000305082175)

Publications and source records attributed to Rondinelli, James M. (ORCID:0000000305082175).

12 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Nanometer-Scale Acoustic Wave Packets Generated by Stochastic Core-Level Photoionization Events

We demonstrate that the absorption of femtosecond hard x-ray pulses excites quasispherical, high-amplitude, and high-wave-vector coherent acoustic phonon wave packets using an all hard-x-ray pump-probe scattering experiment. The time- and momentum-resolved diffuse scattering signal is consistent with an ensemble of 3D strain wave packets induced by the rapid electron cascade dynamics following photoionization at uncorrelated excitation centers. We quantify key parameters of this process, including the localization size of the stress field and the photon energy conversion efficiency into elastic energy. The parameters are determined by the photoelectron and Auger electron cascade dynamics, as well as the electron-phonon interaction. In particular, we obtain the localization size of the observed strain wave packet to be 1.5 and 2.5 nm for bulk SrTiO 3 and KTaO 3 single crystals, respectively. The results provide crucial information on the mechanism of x-ray energy deposition into matter and shed light on the shortest collective length scales accessible to coherent acoustic phonon generation using x-ray excitation. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗
DOE OSTI2024

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024

Zhang, Hengrui (ORCID:0000000231831654)↗
DOE OSTI2024

Systematic improvements in transmon qubit coherence enabled by niobium surface encapsulation

Abstract We present a transmon qubit fabrication technique that yields systematic improvements in T 1 relaxation times. We encapsulate the surface of niobium and prevent the formation of its lossy surface oxide. By maintaining the same superconducting metal and only varying the surface, this comparative investigation examining different capping materials, such as tantalum, aluminum, titanium nitride, and gold, as well as substrates across different qubit foundries demonstrates the detrimental impact that niobium oxides have on coherence times of superconducting qubits, compared to native oxides of tantalum, aluminum or titanium nitride. Our surface-encapsulated niobium qubit devices exhibit T 1 relaxation times 2–5 times longer than baseline qubit devices with native niobium oxides. When capping niobium with tantalum, we obtain median qubit lifetimes above 300 μs, with maximum values up to 600 μs. Our comparative structural and chemical analysis provides insight into why amorphous niobium oxides may induce higher losses compared to other amorphous oxides.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Interlayer magnetophononic coupling in MnBi2Te4

Abstract The emergence of magnetism in quantum materials creates a platform to realize spin-based applications in spintronics, magnetic memory, and quantum information science. A key to unlocking new functionalities in these materials is the discovery of tunable coupling between spins and other microscopic degrees of freedom. We present evidence for interlayer magnetophononic coupling in the layered magnetic topological insulator MnBi 2 Te 4 . Employing magneto-Raman spectroscopy, we observe anomalies in phonon scattering intensities across magnetic field-driven phase transitions, despite the absence of discernible static structural changes. This behavior is a consequence of a magnetophononic wave-mixing process that allows for the excitation of zone-boundary phonons that are otherwise ‘forbidden’ by momentum conservation. Our microscopic model based on density functional theory calculations reveals that this phenomenon can be attributed to phonons modulating the interlayer exchange coupling. Moreover, signatures of magnetophononic coupling are also observed in the time domain through the ultrafast excitation and detection of coherent phonons across magnetic transitions. In light of the intimate connection between magnetism and topology in MnBi 2 Te 4 , the magnetophononic coupling represents an important step towards coherent on-demand manipulation of magnetic topological phases.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Featureless adaptive optimization accelerates functional electronic materials design

Electronic materials that exhibit phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine learning methods display limited predictive capability due to data scarcity and the absence of features that impede model training. In this article, we demonstrate a discovery strategy based on multi-objective Bayesian optimization to directly circumvent these bottlenecks by utilizing latent variable Gaussian processes combined with high-fidelity electronic structure calculations for validation in the chalcogenide lacunar spinel family. We directly and simultaneously learn phase stability and bandgap tunability from chemical composition alone to efficiently discover all superior compositions on the design Pareto front. Previously unidentified electronic transitions also emerge from our featureless adaptive optimization engine. Our methodology readily generalizes to optimization of multiple properties, enabling co-design of complex multifunctional materials, especially where prior data is sparse.

36 MATERIALS SCIENCE↗