arXiv subjects

Andrew R. McCluskey

Publications and source records attributed to Andrew R. McCluskey.

12 recordsLinked to original sources

arXiv2026

Lost in Translation: Simulation-Informed Bayesian Inference Improves Understanding of Molecular Motion From Neutron Scattering

Quasi-elastic neutron scattering (QENS) probes atomic and molecular motion on length and time scales central to catalysis, energy materials, and gas adsorption. However, conventional analytical fitting of QENS spectra often fails to uniquely determine the underlying dynamics. The flexibility of simplified line-shape models can make spectra generated by distinct physical processes statistically indistinguishable, leading to ambiguous or inaccurate mechanistic interpretation. By integrating molecular dynamics simulations, physically derived $Q$-dependent scattering models, Bayesian model discrimination, and polarisation analysis, we demonstrate that QENS can, for the first time, resolve anisotropic rotational motion in liquid benzene, a prototypical aromatic molecule relevant to microporous catalysis. The extracted spinning and tumbling diffusion coefficients suggest stronger anisotropy than previously recognised. This integrated, Bayesian evidence-based analytical framework defines a new paradigm for QENS, enabling direct resolution of the rotational and translational dynamics that govern molecular interactions and transport; the fundamental processes and rate-limiting steps in confined hydrocarbon catalysis.

physics.chem-ph↗

arXiv2026

Bayesian Methods for the Investigation of Temperature-Dependence in Conductivity

Temperature-dependent transport data, including diffusion coefficients and ionic conductivities, are routinely analysed by fitting empirical models such as the Arrhenius equation. These fitted models yield parameters such as the activation energy, and can be used to extrapolate to temperatures outside the measured range. Researchers frequently face challenges in this analysis: quantifying the uncertainty of fitted parameters, assessing whether the data quality is sufficient to support a particular empirical model, and using these models to predict behaviour at temperatures outside the measured range. Bayesian methods offer a coherent framework that addresses all of these challenges. This tutorial introduces the use of Bayesian methods for analysing temperature-dependent transport data, covering parameter estimation, model selection, and extrapolation with uncertainty propagation, with illustrative examples from molecular dynamics simulations of superionic materials.

cond-mat.mtrl-sci↗

arXiv2026

Accurate Estimation of Diffusion Coefficients and their Uncertainties from Computer Simulation

Self-diffusion coefficients, $D^*$, are routinely estimated from molecular dynamics simulations by fitting a linear model to the observed mean-squared displacements (MSDs) of mobile species. MSDs derived from simulation exhibit statistical noise that causes uncertainty in the resulting estimate of $D^*$. An optimal scheme for estimating $D^*$ minimises this uncertainty, i.e., it will have high statistical efficiency, and also gives an accurate estimate of the uncertainty itself. We present a scheme for estimating $\D$ from a single simulation trajectory with high statistical efficiency and accurately estimating the uncertainty in the predicted value. The statistical distribution of MSDs observable from a given simulation is modelled as a multivariate normal distribution using an analytical covariance matrix for an equivalent system of freely diffusing particles, which we parameterise from the available simulation data. We use Bayesian regression to sample the distribution of linear models that are compatible with this multivariate normal distribution, to obtain a statistically efficient estimate of $D^*$ and an accurate estimate of the associated statistical uncertainty.

cond-mat.stat-mech↗

arXiv2026

On the Estimation of Centre of Mass in Periodic Systems

Calculation of the centre of mass of a group of particles in a periodically-repeating cell is an important aspect of chemical and physical simulation. One popular approach calculates the centre of mass via the projection of the individual particles' coordinates onto a circle [Bai \& Breen, \emph{J. Graph. Tools}, \textbf{13}(4), 53, (2008)]. However, this approach involves averaging of the particles in a non-physically meaningful way resulting in inaccurate centres of mass. Instead the intrinsic weighted average should be computed, but the analytical calculation of this is computationally expensive and complex. Here, we propose a more computationally efficient approach to compute the intrinsic mean that is suitable for the majority of chemical systems.

physics.chem-ph↗

arXiv2024

Investigating model influence on the analytical resolution of neutron reflectometry

Neutron reflectometry is a critical tool for investigating the structure of thin films and interfaces. However, the misapplication of the Born approximation to reflection geometry leads some to assume that the minimum thickness that may be probed by neutron reflectometry is limited by the Q-range of the measurement. In this study, we use model-dependent analysis, multiple isotopic contrasts, and magnetic spin states, to show that it is possible to resolve structures significantly smaller than this perceived limit. To quantify this "analytical resolution", we employ Bayesian model selection, offering a robust and quantifiable comparison between different analytical models. We believe that this work offers pivotal insights for the analysis of neutron reflectometry and hope that it will contribute to more accurate and information-rich analyses in the future.

cond-mat.soft↗

arXiv2023

Advice on describing Bayesian analysis of neutron and X-ray reflectometry

Driven by the availability of modern software and hardware, Bayesian analysis is becoming more popular in neutron and X-ray reflectometry analysis. The understandability and replicability of these analyses may be harmed by inconsistencies in how the probability distributions central to Bayesian methods are represented in the literature. Herein, we provide advice on how to report the results of Bayesian analysis as applied to neutron and X-ray reflectometry. This includes the clear reporting of initial starting conditions, the prior probabilities, and results of any analysis, and the posterior probabilities that are the Bayesian equivalent of the error bar, to enable replicability and improve understanding. We believe that this advice, grounded in our experience working in the field, will enable greater analytical reproducibility among the reflectometry community, as well as improve the quality and usability of results.

stat.AP↗

arXiv2021

Overscreening and Underscreening in Solid-Electrolyte Grain Boundary Space-Charge Layers

Polycrystalline solids can exhibit material properties that differ significantly from those of equivalent single-crystal samples, in part, because of a spontaneous redistribution of mobile point defects into so-called space-charge regions adjacent to grain boundaries. The general analytical form of these space-charge regions is known only in the dilute limit, where defect-defect correlations can be neglected. Using kinetic Monte Carlo simulations of a three-dimensional Coulomb lattice gas, we show that grain-boundary space-charge regions in non-dilute solid electrolytes exhibit overscreening -- damped oscillatory space-charge profiles -- and underscreening -- decay lengths that are longer than the corresponding Debye length and that increase with increasing defect-defect interaction strength. Overscreening and underscreening are known phenomena in concentrated liquid electrolytes, and the observation of functionally analogous behaviour in solid electrolyte space-charge regions suggests that the same underlying physics drives behaviour in both classes of systems. We therefore expect theoretical approaches developed to study non-dilute liquid electrolytes to be equally applicable to future studies of solid electrolytes.

cond-mat.mtrl-sci↗

arXiv2020

Bayesian determination of the effect of a deep eutectic solvent on the structure of lipid monolayers

In this work, we present the first example of the self-assembly of phospholipid monolayers at the interface between air and an ionic solvent. Deep eutectic solvents are a novel class of environmentally friendly non-aqueous room temperature liquids with tunable properties, that have wide ranging potential applications and are capable of promoting the self-assembly of surfactant molecules. We use a chemically-consistent Bayesian modelling of X-ray and neutron reflectometry measurements to show that these monolayers broadly behave as they do on water. This method allows for the monolayer structure to be determined, alongside the molecular volumes of the individual monolayer components without the need for water-specific constraints to be introduced. Furthermore, using this method we are able to better understand the correlations present between parameters in the analytical model. This example of a non-aqueous phospholipid monolayer has important implications for the potential uses of these solvents and for our understanding of how biomolecules behave in the absence of water.

cond-mat.soft↗

arXiv2020

Assessing molecular simulation for the analysis of lipid monolayer reflectometry

Using molecular simulation to aid in the analysis of neutron reflectometry measurements is commonplace. However, reflectometry is a tool to probe large-scale structures, and therefore the use of all-atom simulation may be irrelevant. This work presents the first direct comparison between the reflectometry profiles obtained from different all-atom and coarse-grained molecular dynamics simulations. These are compared with a traditional model layer structure analysis method to determine the minimum simulation resolution required to accurately reproduce experimental data. We find that systematic limits reduce the efficacy of the MARTINI potential model, while the Berger united-atom and Slipids all-atom potential models agree similarly well with the experimental data. The model layer structure gives the best agreement, however, the higher resolution simulation-dependent methods produce an agreement that is comparable. Finally, we use the atomistic simulation to advise on possible improvements that may be offered to the model layer structures, creating a more realistic monolayer model.

cond-mat.soft↗

arXiv2020

A general approach to maximise information density in neutron reflectometry analysis

Neutron and X-ray reflectometry are powerful techniques facilitating the study of the structure of interfacial materials. The analysis of these techniques is ill-posed in nature requiring the application of a model-dependent methods. This can lead to the over- and under- analysis of experimental data, when too many or too few parameters are allowed to vary in the model. In this work, we outline a robust and generic framework for the determination of the set of free parameters that is capable of maximising the in-formation density of the model. This framework involves the determination of the Bayesian evidence for each permutation of free parameters; and is applied to a simple phospholipid monolayer. We believe this framework should become an important component in reflectometry data analysis, and hope others more regularly consider the relative evidence for their analytical models.

cond-mat.soft↗

arXiv2020

An introduction to classical molecular dynamics simulation for experimental scattering users

Classical molecular dynamics simulations are a common component of multi-modal analyses from scattering measurements, such as small-angle scattering and diffraction. Users of these experimental techniques often have no formal training in the theory and practice of molecular dynamics simulation, leading to the possibility of these simulations being treated as a "black box" analysis technique. In this article, we describe an open educational resource (OER) designed to introduce classical molecular dynamics to users of scattering methods. This resource is available as a series of interactive web pages, which can be easily accessed by students, and as an open source software repository, which can be freely copied, modified, and redistributed by educators. The topic covered in this OER includes classical atomistic modelling, parameterising interatomic potentials, molecular dynamics simulations, typical sources of error, and some of the approaches to using simulations in the analysis of scattering data.

cond-mat.stat-mech↗

arXiv2020

Neutron reflectometry analysis: using model-dependent methods

Neutron reflectometry analysis is an inherently ill-posed, which is to say that there are many possible solutions which agree equally well with the measured data. This leads to the application of model-dependent analysis, where information that we know about the system is integrated into our analysis. This tutorial briefly covers the mathematics underlying the use of model-dependent analysis in neutron reflectometry.

stat.AP↗