Tables for
Volume H
Powder diffraction
Edited by C. J. Gilmore, J. A. Kaduk and H. Schenk

International Tables for Crystallography (2018). Vol. H, ch. 3.4, p. 275

Section Monte Carlo search method

A. Altomare,a* C. Cuocci,a A. Moliternia and R. Rizzia

aInstitute of Crystallography – CNR, Via Amendola 122/o, Bari, I-70126, Italy
Correspondence e-mail: Monte Carlo search method

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The Monte Carlo approach has also been applied to indexing powder diffraction patterns (Le Bail, 2004[link]; Bergmann et al., 2004[link]; Le Bail, 2008[link]). It exploits all the information contained in the full pattern, randomly generates and selects trial cell parameters, and calculates peak positions to which it assigns the corresponding Miller indices. An idealized powder pattern consisting of peak positions d and extracted intensities I is considered to test the trial cell. The cell reliability is assessed by suitable figures of merit (e.g. Rp and McM20, see Section[link]). The main drawback of this approach is the significant computing time required, in particular for triclinic systems.


Bergmann, J., Le Bail, A., Shirley, R. & Zlokazov, V. (2004). Renewed interest in powder diffraction data indexing. Z. Kristallogr. 219, 783–790.Google Scholar
Le Bail, A. (2004). Monte Carlo indexing with McMaille. Powder Diffr. 19, 249–254.Google Scholar
Le Bail, A. (2008). Structure solution. In Principles and Applications of Powder Diffraction, edited by A. Clearfield, J. H. Reibenspies & N. Bhuvanesh, pp. 261–309. Oxford: Wiley-Blackwell.Google Scholar

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