arXiv subjects

Brian Donovan

Publications and source records attributed to Brian Donovan.

8 recordsLinked to original sources

arXiv2025

Machine Learning for Predicting Magnetization from X-ray Diffraction of Iron Oxide Nanoparticles Using Simple Physics-Based Data Generation

Automation and high-throughput characterization and synthesis for material development are becoming increasingly common; these approaches require machine learning (ML) tools to assess material properties, ideally based on a single measurement. Here, ML models are developed to predict magnetization from X-ray diffraction (XRD) for iron oxide nanoparticles. Our approach is to first develop a set of simulated data that links modulated XRD, based on a crystallographic information file (CIF), to a simple magnetic model to determine magnetization at a given magnetic field, thereby enabling us to train Random Forest and Gradient Boosting regression models on a large amount of simulated data. The models are validated by synthesizing iron oxide nanoparticles and measuring their crystal structure via XRD and room-temperature magnetization curves. In doing so, we can fine-tune both the training hyperparameters and the optimal size of the simulated datasets used to train the models. Through this optimization, the best models can achieve an $R^2$ greater than 0.9 for five experimental samples, used for tuning, for predicting the max magnetization (at 2.8 T) of the measurement. Lastly, we demonstrate reasonable predictions on the full magnetization vs. magnetic field curve, showing that the RF model excels at predicting the high magnetic field values, which is key for determining the success of an iron oxide nanoparticle synthesis for applications like magnetic particle imaging (MPI), thermal magnetic particle imaging (T-MPI), and hyperthermia.

cond-mat.mtrl-sci↗

arXiv2025

Probing Magnetic Properties of RuO$_{2}$ Heterostructures Through the Ferromagnetic Layer

RuO$_{2}$ has been proposed as the prototypical altermagnetic material. However, several reports have recently questioned its intrinsic magnetic ordering, leading to conflicting findings, especially in thin film heterostructures pointing to possible interface effects being convoluted with supposed antiferromagnetic/altermagnetic signatures. Here, extensive magnetometry measurements were performed on two independently grown thin film heterostructures of RuO$_{2}$ interfaced with either NiFe or Fe acting as the ferromagnetic layer. Below about 15 K, both samples exhibit exchange bias fields when cooled to approximately 2 K in a $+$1 T field, and a spin transitional feature is observed around 31 K. Magneto-Raman measurements on RuO$_{2}$ thin films only reveal a magnon mode when there is a NiFe layer, suggesting that RuO$_{2}$ does not intrinsically possess long range magnetic ordering.. When in contact with a ferromagnet, RuO$_2$ displays effects that could be ascribed to antiferromagnetism. However, the lack of intrinsic magnon modes points toward possible diffusion between the layers or spin disorder at the interface as seen by density functional theory (DFT) calculations.

cond-mat.mtrl-sci↗

arXiv2024

Quantum Monte Carlo and density functional theory study of strain and magnetism in 2D 1T-VSe$_2$ with charge density wave states

Two-dimensional (2D) 1T-VSe$_2$ has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) states and the interplay between the two. We employed a combined Diffusion Monte Carlo (DMC) and density functional theory (DFT) approach to accurately investigate the magnetic properties, CDW states, and their response to strain in monolayer 1T-VSe$_2$. Our calculations show the delicate competition between various phases, revealing critical insights into the relationship between their energetic and structural properties. We performed classical Monte Carlo simulations informed by our DMC and DFT results, and found the magnetic transition temperature ($T_c$) of the undistorted (non-CDW) FM phase to be 228 K and the distorted (CDW) phase to be 68 K. Additionally, we studied the response of biaxial strain on the energetic stability and magnetic properties of various phases of 2D 1T-VSe$_2$ and found that small amounts of strain can increase the $T_c$, suggesting a promising route for engineering and enhancing magnetic behavior. Finally, we synthesized 1T-VSe$_2$ and performed Raman spectroscopy measurements, which were in close agreement with our calculated results, validating our computational approach. Our work emphasizes the role of highly accurate DMC methods in advancing the understanding of monolayer 1T-VSe$_2$ and provides a robust framework for future studies of 2D magnetic materials.

cond-mat.mtrl-sci↗

arXiv2024

Hydrogen-doping mediated solid state thermal switch

Recent reports reveal that isothermal chemical doping of hydrogen in correlated complex oxides such as perovskite nickelates (e.g. NdNiO3) can induce a metal-to-insulator transition (MIT) without the need for temperature modulation. In this work, we interrogate the magnitude change in temperature dependence of thermal conductivity upon chemical doping of hydrogen, as any changes to the thermal properties upon doping offer a route to solid-state thermal switches as well as another potential signal to monitor in a diverse set of sensing, electronic, and optical applications. Using frequency-domain thermoreflectance, we demonstrate that a large concentration of hydrogen (~ 0.1 - 0.5 H/unit cell) completely suppresses the electronic contribution to thermal conductivity in NdNiO3 thin films and reduces the phononic contribution by a factor of 2. These results are critical for the design of next-generation solid-state thermal switches, sensors, extreme environment electronics and neuromorphic memory architectures.

cond-mat.mtrl-sci↗

arXiv2021

High-Fidelity Control of Superconducting Qubits Using Direct Microwave Synthesis in Higher Nyquist Zones

Control electronics for superconducting quantum processors have strict requirements for accurate command of the sensitive quantum states of their qubits. Hinging on the purity of ultra-phase-stable oscillators to upconvert very-low-noise baseband pulses, conventional control systems can become prohibitively complex and expensive when scaling to larger quantum devices, especially as high sampling rates become desirable for fine-grained pulse shaping. Few-GHz radio-frequency digital-to-analog converters (RF DACs) present a more economical avenue for high-fidelity control while simultaneously providing greater command over the spectrum of the synthesized signal. Modern RF DACs with extra-wide bandwidths are able to directly synthesize tones above their sampling rates, thereby keeping the system clock rate at a level compatible with modern digital logic systems while still being able to generate high-frequency pulses with arbitrary profiles. We have incorporated custom superconducting qubit control logic into off-the-shelf hardware capable of low-noise pulse synthesis up to 7.5 GHz using an RF DAC clocked at 5 GHz. Our approach enables highly linear and stable microwave synthesis over a wide bandwidth, giving rise to high-resolution control and a reduced number of required signal sources per qubit. We characterize the performance of the hardware using a five-transmon superconducting device and demonstrate consistently reduced two-qubit gate error (as low as 1.8%) which we show results from superior control chain linearity compared to traditional configurations. The exceptional flexibility and stability further establish a foundation for scalable quantum control beyond intermediate-scale devices.

quant-ph↗

arXiv2019

Real-time decoding of stabilizer measurements in a bit-flip code

Although qubit coherence times and gate fidelities are continuously improving, logical encoding is essential to achieve fault tolerance in quantum computing. In most encoding schemes, correcting or tracking errors throughout the computation is necessary to implement a universal gate set without adding significant delays in the processor. Here we realize a classical control architecture for the fast extraction of errors based on multiple cycles of stabilizer measurements and subsequent correction. We demonstrate its application on a minimal bit-flip code with five transmon qubits, showing that real-time decoding and correction based on multiple stabilizers is superior in both speed and fidelity to repeated correction based on individual cycles. Furthermore, the encoded qubit can be rapidly measured, thus enabling conditional operations that rely on feed-forward, such as logical gates. This co-processing of classical and quantum information will be crucial in running a logical circuit at its full speed to outpace error accumulation.

quant-ph↗

arXiv2017

Hardware for Dynamic Quantum Computing

We describe the hardware, gateware, and software developed at Raytheon BBN Technologies for dynamic quantum information processing experiments on superconducting qubits. In dynamic experiments, real-time qubit state information is fedback or fedforward within a fraction of the qubits' coherence time to dynamically change the implemented sequence. The hardware presented here covers both control and readout of superconducting qubits. For readout we created a custom signal processing gateware and software stack on commercial hardware to convert pulses in a heterodyne receiver into qubit state assignments with minimal latency, alongside data taking capability. For control, we developed custom hardware with gateware and software for pulse sequencing and steering information distribution that is capable of arbitrary control flow on a fraction superconducting qubit coherence times. Both readout and control platforms make extensive use of FPGAs to enable tailored qubit control systems in a reconfigurable fabric suitable for iterative development.

quant-ph↗

arXiv2015

Using coarse GPS data to quantify city-scale transportation system resilience to extreme events

This article proposes a method to quantitatively measure the resilience of transportation systems using GPS data from taxis. The granularity of the GPS data necessary for this analysis is relatively coarse; it only requires coordinates for the beginning and end of trips, the metered distance, and the total travel time. The method works by computing the historical distribution of pace (normalized travel times) between various regions of a city and measuring the pace deviations during an unusual event. This method is applied to a dataset of nearly 700 million taxi trips in New York City, which is used to analyze the transportation infrastructure resilience to Hurricane Sandy. The analysis indicates that Hurricane Sandy impacted traffic conditions for more than five days, and caused a peak delay of two minutes per mile. Practically, it identifies that the evacuation caused only minor disruptions, but significant delays were encountered during the post-disaster reentry process. Since the implementation of this method is very efficient, it could potentially be used as an online monitoring tool, representing a first step toward quantifying city scale resilience with coarse GPS data.

physics.soc-ph↗