arXiv subjects

Scott M. Woodley

Publications and source records attributed to Scott M. Woodley.

5 recordsLinked to original sources

arXiv2026

Scientific applications of quantum computing: challenges and opportunities

The predictive simulation of molecules and materials has had a broad and significant impact. It nevertheless remains constrained by the cost of accurately treating electronic correlation, excited states, and complex energy landscapes. Quantum computing offers a fundamentally different computational paradigm in which quantum states are encoded and manipulated directly rather than approximated on classical hardware. Here we discuss where this approach may provide a genuine scientific advantage in chemistry, materials science, and biochemistry. Promising directions include the high-accuracy treatment of correlated active spaces, improved excited-state simulations, and accelerated exploration of combinatorial structure spaces. The central challenge is therefore not qubit scaling alone, but demonstrably chemically meaningful gains in predictive reliability. We argue that near-term value is most likely to come from disciplined workflow integration rather than wholesale replacement of classical methods. Noisy physical devices, error-mitigated utility experiments, early fault-tolerant devices, and fully fault-tolerant quantum computers offer different scientific prospects, and claims of usefulness must be tied to the specific regime being discussed. Quantum computing will become scientifically valuable when it demonstrably reduces uncertainty in computed energies, rates, spectra, or materials stability after the full costs of state preparation, measurement, error handling, and coupling to classical simulation are included.

quant-ph↗

arXiv2026

Thermodynamic sampling of materials using neutral-atom quantum computers

Neutral-atom quantum hardware has emerged as a promising platform for programmable many-body physics. In this work, we develop and validate a practical framework for extracting thermodynamic properties of materials using such hardware. As a test case, we consider nitrogen-doped graphene. Starting from Density Functional Theory (DFT) formation energies, we map the material energetics onto a Rydberg-atom Hamiltonian suitable for quantum annealing by fitting an on-site term and distance-dependent pair interactions. The Hamiltonian derived from DFT cannot be implemented directly on current QuEra devices, as the largest energy scale accessible on the hardware is two orders of magnitude smaller than the target two-body interaction in the material. To overcome this limitation, we introduce a rescaling strategy based on a single parameter, $α_v$, which ensures that the distribution sampled by the hardware is well described by Boltzmann-like weights corresponding to those of the material at an effective temperature $T^{\prime} = α_v T$, where $T$ is the device sampling temperature. This rescaling also establishes a direct correspondence between the global laser detuning $Δ_g$ and the grand-canonical chemical potential $Δμ$. We validate the method on a 28-site graphene nanoflake using exhaustive enumeration, and on a larger 78-site system where Monte Carlo sampling confirms preferential sampling of low-energy configurations.

quant-ph↗

arXiv2026

Materials Informatics Across the Length Scales

Materials informatics is increasingly used to support modelling, analysis and design across the length scales of materials science, from atomistic simulations to microstructural characterisation and continuum descriptions. Despite rapid progress, the reliability and transferability of these approaches vary strongly with scale. Here we survey data-driven methods at the nanoscale, mesoscale, and micro-to-continuum levels, highlighting established capabilities as well as unresolved challenges. Machine-learning interatomic potentials, mesoscale surrogate and operator-learning models, and learning-based analysis of experimental microstructures are discussed, with emphasis on data quality, uncertainty, interpretability, and cross-scale consistency. We further examine the role of data standards, ontologies, and emerging tools, such as autonomous laboratories, where they directly affect multiscale workflows. This perspective clarifies what can be considered reliable today and identifies key obstacles to the broader integration of materials informatics across scales.

cond-mat.mtrl-sci↗

arXiv2024

Modelling Silica using MACE-MP-0 Machine Learnt Interatomic Potentials

Silica polymorphs and zeolites are fundamental to a wide range of industrial applications owing to their diverse structural characteristics, thermodynamic and mechanical stability under varying conditions and due to their geological importance. Computational modelling has played a crucial role in understanding the relationship between the structure and functionality of silicas and silicates including zeolites. In this study, we apply the MACE-MP-0 machine learnt interatomic potentials (ML-IP) to model the framework energies of siliceous zeolites and examine the phase transitions of silica and ZSM-5 polymorphs under high-pressure conditions. The results reproduce the known metastability of siliceous zeolites relative to α-quartz, with energy differences between microporous and dense phases calculated by MACE-MP-0 medium ML-IP and density functional theory (DFT) methods closely aligning with experimental calorimetric data. The high-pressure simulations reveal distinct compression behaviour in the quartz, coesite, and stishovite polymorphs of silica, with coesite and stishovite showing increased stability at elevated pressures in line with experimental data. The calculated phase transition pressures from quartz to coesite (~3.5 GPa) and coesite to stishovite (~9 GPa) are close to experimental findings, demonstrating the reliability of MACE ML-IP in modelling the structural and energetic properties of silica polymorphs.

cond-mat.mtrl-sci↗

arXiv2019

Donor and Acceptor Characteristics of Native Point Defects in GaN

The semiconducting behaviour and optoelectronic response of gallium nitride is governed by point defect processes, which, despite many years of research, remain poorly understood. The key difficulty in the description of the dominant charged defects is determining a consistent position of the corresponding defect levels, which is difficult to derive using standard supercell calculations. In a complementary approach, we take advantage of the embedded cluster methodology that provides direct access to a common zero of the electrostatic potential for all point defects in all charge states. Charged defects polarise a host dielectric material with long-range forces that strongly affect the outcome of defect simulations; to account for the polarisation we couple embedding with the hybrid quantum mechanical/molecular mechanical (QM/MM) approach and investigate the structure, formation and ionisation energies, and equilibrium concentrations of native point defects in wurtzite GaN at a chemically accurate hybrid-density-functional-theory level. N vacancies are the most thermodynamically favourable native defects in GaN, which contribute to the n-type character of as-grown GaN but are not the main source, a result that is consistent with experiment. Our calculations show no native point defects can form thermodynamically stable acceptor states. GaN can be easily doped n-type, but, in equilibrium conditions at moderate temperatures acceptor dopants will be compensated by N vacancies and no significant hole concentrations will be observed, indicating non-equilibrium processes must dominate in p-type GaN. We identify spectroscopic signatures of native defects in the infrared, visible and ultraviolet luminescence ranges and complementary spectroscopies. Crucially, we calculate the effective-mass-like-state levels associated with electrons and holes bound in diffuse orbitals...

cond-mat.mtrl-sci↗