When can we trust structural models derived from pair distribution function measurements?

20 May 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The pair distribution function (PDF) is an important metric for characterising structure in complex materials, but it is well known that meaningfully different structural models can sometimes give rise to equivalent PDFs. In this paper, we discuss the use of model likelihoods as a general approach for discriminating between such homometric structure solutions. Drawing on two main case studies---one concerning the structure of a small peptide and the other amorphous calcium carbonate---we show how consideration of model likelihood can help drive robust structure solution even in cases where the PDF is particularly information poor. The obvious thread of these individual case studies is the potential role for machine learning approaches to help guide structure determination from the PDF, and our paper finishes with some forward-looking discussion along these lines.


pair distribution function
structure solution
inverse methods


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.