Seminar Andrew McCluskey (online)
Bayesian Methods To Study Atomic Dynamics in Simulation and by Neutron Scattering
In this seminar, I will introduce two recent methodological developments that improve our ability to study the dynamics of atoms and molecules.
First, I will outline the “routine” practice of estimating a self-diffusion coefficient from a molecular dynamics simulation, by “fitting a straight line” to the observed mean-squared displacements (MSDs). Typically, this fitting is performed without considering fundamental concepts of displacement, such as their heteroscedasticity and correlation, leading to suboptimal estimation. I will then present a Bayesian scheme for estimating the self-diffusion coefficient from a single simulation trajectory with high statistical efficiency and accurate quantification of the uncertainty in the predicted value [1, 2].
Following this, I will discuss how simulation and Bayesian inference can inform the analysis of quasi-elastic neutron scattering (QENS) data. Recently, we have shown that by constructing a mathematical model based on simulation observations, we can maximise the information from a QENS experiment [3]. We apply this approach to liquid benzene and, for the first time with QENS, observe the anisotropic rotation of benzene.
Together, these developments set the stage for a new way to use neutron spectroscopic measurements of liquid systems. By combining simulation and QENS measurements, it will be possible to probe, in a single sample, both self and collective molecular dynamics.
You may enjoy this seminar if you are interested in data analysis, computational modelling, liquid dynamics, neutron scattering, or open-source software.
1. A. R. McCluskey, S. W. Coles and B. J. Morgan, J. Chem. Theory Comput., 21 (1), 79, 2025.
2. A. R. McCluskey, A. G. Squires, J. Dunn, S. W. Coles and B. J. Morgan, J. Open Source Softw., 9, 5984, 2024.
3. H. Richardson, K. McColl, G. J. Nilsen, J. Armstrong, A. R. McCluskey, J. Phys. Chem. Lett., Accepted, 2026.