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When can we trust structural models derived from pair distribution function measurements?
Phillip M Maffettone1, William J K Fletcher1, Thomas C Nicholas1
1Department of Chemistry, University of Oxford, Inorganic Chemistry Laboratory, South Parks Road, Oxford OX1 3QR, UK. andrew.goodwin@chem.ox.ac.uk.
Faraday Discussions
|September 11, 2024
Summary
Model likelihoods help distinguish between similar structural models that produce identical pair distribution functions (PDFs). This approach aids in robust structure determination, even with limited PDF data, and suggests machine learning
Area of Science:
- Materials Science
- Structural Chemistry
- Computational Materials Science
Background:
- The pair distribution function (PDF) is crucial for characterizing complex material structures.
- Homometric structures, which are distinct models yielding identical PDFs, pose a significant challenge in structural analysis.
Purpose of the Study:
- To introduce and demonstrate the utility of model likelihoods for discriminating between homometric structure solutions.
- To showcase the application of model likelihoods in challenging structure determination scenarios.
Main Methods:
- Utilized model likelihoods as a general framework for distinguishing between equivalent PDF models.
- Applied the approach to two case studies: a small peptide and amorphous calcium carbonate.
Main Results:
- Demonstrated that model likelihoods effectively differentiate between homometric structures.
- Showcased the successful application of model likelihoods in information-poor PDF scenarios.
Conclusions:
- Model likelihoods provide a robust method for resolving structural ambiguities arising from equivalent PDFs.
- The findings highlight the potential of machine learning to enhance PDF-based structure determination.
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