Developing the next generation of analysis

X-ray and neutron reflectometry measurements are powerful techniques used to probe the nanoscale structure of interfaces. These measurements may be performed at large scale facilities such as the Diamond Light Source (Beamline I07) or the ISIS Neutron and Muon Facilities. However, as the application of this technique is becoming more varied, it is more and more necessary for non-expert users to perform experiments, and analyse the resulting datasets. This means that it is necessary to data analysis procedures to evolve, moving towards more informed and automated data analysis.

This work involves the development of chemically-consistent analysis methods for experimental system, moving towards an “off-the-shelf” automated analysis process. Such an analytical method has been developed for the study of lipid monolayer [1]. We are also developing approaches based on Bayesian inference to determine the most probable model for a given experimental data set, initially building on findings obtained from atomistic simulation [2].

Through the Ada Lovelace Centre funded project we are aiming to develop a data standard to reduced, experimental reflectometry data. The hope is that this standard will allow for straightforward tagging of the experimental data in order to produce extensive data catalogues that may be subject to data mining and machine learning applications.


  1. A. R. McCluskey, A. Sanchez-Fernandez, K. J. Edler, S. C. Parker, A. J. Jackson, R. A. Campbell, & T. Arnold. Phys. Chem. Chem. Phys., 21(11), 6133-6141, 2019. DOI: 10.1039/C9CP00203K.
  2. A. R. McCluskey, J. Grant, A. J. Smith, J. L. Rawle, D. J. Barlow, M. J. Lawrence, S. C. Parker, & K. J. Edler. J. Phys. Comm., Accepted Manuscript, 2019. DOI: 10.1088/2399-6528/ab12a9