arXiv subjects

Ned Thaddeus Taylor

Publications and source records attributed to Ned Thaddeus Taylor.

6 recordsLinked to original sources

arXiv2026

Compact Variational Neural Networks for Spectral Inference from a Single Nonlinear 2D Perovskite Photodetector

Spectroscopy conventionally separates optical frequencies before detection, imposing persistent constraints on footprint, complexity and scalability. Here we establish an alternative paradigm in which the nonlinear optoelectronic dynamics of a single two-dimensional perovskite photodetector physically encode the incident optical field and machine learning performs the inverse spectral reconstruction. Using a planar fluorinated phenethylammonium lead iodide (F-PEAI) photodetector, we exploit wavelength- and irradiance-dependent current-voltage signatures arising from the coupled effects of photocarrier generation, trapping, interfacial transport and field-dependent carrier dynamics. A compact variational encoder-decoder preserves the functional and history-dependent structure of these responses by independently projecting forward and reverse voltage sweeps onto a truncated Legendre-polynomial basis before mapping them through a probabilistic latent representation to continuous spectral parameters. Trained on fewer than 400 experimental voltage sweeps, the model generalises to excitation wavelengths excluded from training, reconstructing wavelength with $R^2=0.958$ and a mean absolute error of 8.1 nm, while recovering log-normalised irradiance with $R^2=0.987$. Voltage-resolved analysis further reveals that wavelength and irradiance are encoded differently across the nonlinear device response, with distinct bias regions carrying complementary optical information. These results establish nonlinear material and interface dynamics as a computational resource for spectroscopy and point towards hardware-algorithm co-design in which materials, interfaces and inference architectures are engineered jointly to maximise information content, enabling compact spectroscopic systems without dispersive optics or detector arrays.

physics.optics↗

arXiv2026

Atomistic Structure Generation and Neural-Network Screening of Hard Carbons to Identify High-Capacity Sodium Storage

Hard carbons are established anodes for lithium-ion batteries and leading candidates for sodium-ion batteries, yet their electrochemical performance is governed by a heterogeneous network of graphitic domains, defects, and nanopores that conventional atomistic methods cannot model at the required length scales. We combine universal machine-learned interatomic potentials with the RAFFLE structure-generation framework to construct 13,096 realistic hard carbon models containing up to 4,378 atoms, matching experimentally measured densities, porosities, and sp$^2$/sp$^3$ bonding fractions. Explicit sodium intercalation of representative structures reproduces the characteristic sloping-to-plateau voltage profiles, revealing that capacity increases with decreasing carbon density and increasing porosity. To screen the full library, we train a lightweight neural-network surrogate that predicts capacity directly from the host using frozen universal-potential descriptors augmented by geometric void features. The surrogate identifies high-capacity candidates exceeding 800 mAh g$^{-1}$, which are validated by full intercalation calculations. This scalable framework links hard carbon microstructure to sodium-storage performance and provides atomistic design principles for high-capacity anodes. More broadly, the workflow enables systematic exploration of synthesis-dependent amorphous microstructures, including precursor chemistry, pyrolysis, heteroatom doping, and pore engineering, providing a route toward atomistically informed hard carbon design.

cond-mat.mtrl-sci↗

arXiv2026

Phonon driven non-equilibrium triggers for thermal runaway in battery electrodes

Thermal runaway in lithium-ion batteries is governed by the poorly-understood initiation phase, where localised heating introduces instability. Here we identify the three key components that trigger thermal runaway, decreases in local conductivity, heat capacity changes, and intercalation heating, which significantly increase temperature gradients that accelerate battery degradation. Using a multiscale framework that links atomistic phonon calculations with grain-resolved thermal modelling, we identify large thermal gradients across grain boundaries arising from external heating events and intercalation-dependent thermal properties of Li$_x$ZrS$_2$. The observed changes in thermal conductivity are due to charge redistribution and bond-strength modulation of the host, in contrast to the existing theory of lithium rattler mechanics. Internal heating events driven by intercalation gives rise to local thermal gradients, finite-speed thermal wave interference, and internal thermal fluctuations that generate mechanical strain and sub-grain thermal breakdown. These results show that the trigger for thermal runaway is controlled by internal grain architecture and composition, as well as the external environment. Our findings establish materials and electrode design rules for suppressing hotspot formation and improving battery safety during fast charging.

cond-mat.mtrl-sci↗

arXiv2025

Thermal Metamaterials for Enhanced Non-Fourier Heat Transport

The untapped potential of thermal metamaterials requires the simultaneous observation of both diffusive and wave-like heat propagation across multiple length scales that can only be realised through theories beyond Fourier. Here, we demonstrate that tailored material patterning significantly modifies heat transport dynamics with enhanced non-Fourier behaviour. By bridging phonon scattering mechanisms with macroscopic heat flux via a novel perturbation-theory approach, we derive the hyperbolic Cattaneo model directly from particle dynamics, establishing a direct link between relaxation time and phonon lifetimes. Our micro-scale patterned systems exhibit extended non-Fourier characteristics, where internal interfaces mediate wave-like energy propagation, diverging sharply from diffusive Fourier predictions. These results provide a unified framework connecting micro-scale interactions to macroscopic transport, resolving long-standing limitations of the Cattaneo model. This work underscores the transformative potential of thermal metamaterials for ultra-fast thermal management and nanoscale energy applications, laying a theoretical foundation for next-generation thermal technologies.

cond-mat.mtrl-sci↗

arXiv2025

Instability of oxide perovskite surfaces induced by vacancy formation

This work presents a first principles study of the (001) surface energetics of nine oxide perovskites, with a focus on the role of surface vacancies in determining termination stability. Additionally, investigation into the behaviour of vacancies as a function of depth from the surface in these perovskites, ABO$_{3}$ (A=Ca, Sr, Ba; B=Ti, Zr, Sn), is carried out, and results are compared to formation of the vacancies in bulk. Combining results from these investigations reveals a general trend for all nine perovskites - the undefected AO surface is more energetically favourable to form than the BO2 surface. This dominance of the AO over the BO2 surface is further enforced by the phase diagrams of perovskite surfaces. However, A-site vacancies at the AO surface are far more favourable (1-2 eV lower in energy) than B-site vacancies at the BO2 surface. Charged vacancies only drive this further under oxygen-rich conditions, showing a smaller range of stability for the AO than the BO2 surface. These results indicates that, whilst the AO surface is easier to form, the BO2 surface will display better long term stability, making it more suitable for use in potential applications. This study furthers the understanding of oxide perovskite (001)-terminated surface stability, which will aid in surface growth and manufacturing.

cond-mat.mtrl-sci↗

arXiv2025

RAFFLE: Active learning accelerated interface structure prediction

Interfaces between materials play a crucial role in the performance of most devices. However, predicting the structure of a material interface is computationally demanding due to the vast configuration space, which requires evaluating an unfeasibly large number of highly complex structures. We introduce RAFFLE, a software package designed to efficiently explore low-energy interface configurations between any two crystals. RAFFLE leverages physical insights and genetic algorithms to intelligently sample the configuration space, using dynamically evolving 2-, 3-, and 4-body distribution functions as generalised structural descriptors. These descriptors are iteratively updated through active learning, which inform atom placement strategies. RAFFLE's effectiveness is demonstrated across a diverse set of systems, including bulk materials, intercalation structures, and interfaces. When tested on bulk aluminium and MoS$_2$, it successfully identifies known ground-state and high-pressure phases. Applied to intercalation systems, it predicts stable intercalant phases. For Si|Ge interfaces, RAFFLE identifies intermixing as a strain compensation mechanism, generating reconstructions that are more stable than abrupt interfaces. By accelerating interface structure prediction, RAFFLE offers a powerful tool for materials discovery, enabling efficient exploration of complex configuration spaces.

cond-mat.mtrl-sci↗