TY - JOUR

T1 - Powder diffraction data beyond the pattern: a practical review

AU - Casati, Nicola

AU - Boldyreva, Elena

N1 - EB acknowledges partial support of this work by the Ministry of Science and Higher Education of the Russian Federation (project FWUR-2024-0032 for Boreskov lnstitute of Catalysis Siberian Branch of Russian Academy of Sciences).

PY - 2025/8

Y1 - 2025/8

N2 - We share personal experience in the fields of materials science and high-pressure research, discussing which parameters, in addition to positions of peak maxima and intensities, may be important to control and to document in order to make deposited powder diffraction data reusable, reproducible and replicable. We discuss, in particular, which data can be considered as `raw' and some challenges of revisiting deposited powder diffraction data. We consider procedures such as identifying (`fingerprinting') a known phase in a sample, solving a bulk crystal structure from powder data, and analyzing the size of coherently scattering domains, lattice strain, the type of defects or preferred orientation of crystallites. The specific case of characterizing a multi-phase multi-grain sample following in situ structural changes during mechanical treatment in a mill or on hydrostatic compression is also examined. We give examples of when revisiting old data adds a new knowledge and comment on the challenges of using deposited data for machine learning.

AB - We share personal experience in the fields of materials science and high-pressure research, discussing which parameters, in addition to positions of peak maxima and intensities, may be important to control and to document in order to make deposited powder diffraction data reusable, reproducible and replicable. We discuss, in particular, which data can be considered as `raw' and some challenges of revisiting deposited powder diffraction data. We consider procedures such as identifying (`fingerprinting') a known phase in a sample, solving a bulk crystal structure from powder data, and analyzing the size of coherently scattering domains, lattice strain, the type of defects or preferred orientation of crystallites. The specific case of characterizing a multi-phase multi-grain sample following in situ structural changes during mechanical treatment in a mill or on hydrostatic compression is also examined. We give examples of when revisiting old data adds a new knowledge and comment on the challenges of using deposited data for machine learning.

UR - https://www.scopus.com/pages/publications/105012771928

UR - https://www.mendeley.com/catalogue/2adcef64-cac5-3a56-8f45-efc67cab615a/

U2 - 10.1107/s1600576725004728

DO - 10.1107/s1600576725004728

M3 - Article

VL - 58

SP - 1085

EP - 1105

JO - Journal of Applied Crystallography

JF - Journal of Applied Crystallography

SN - 0021-8898

IS - 4

ER -