Towards the extraction of the crystal cell parameters from pair distribution function profiles.

ABSTRACT: The approach based on atomic pair distribution function (PDF) has revolutionized structural investigations by X-ray/electron diffraction of nano or quasi-amorphous materials, opening up the possibility of exploring short-range order. However, the ab initio crystal structural solution by the PDF is far from being achieved due to the difficulty in determining the crystallographic properties of the unit cell. A method for estimating the crystal cell parameters directly from a PDF profile is presented, which is composed of two steps: first, the type of crystal cell is inferred using machine-learning approaches applied to the PDF profile; second, the crystal cell parameters are extracted by means of multivariate analysis combined with vector superposition techniques. The procedure has been vali

SUBMITTER: Guccione P 

PROVIDER: S-EPMC10478520 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature