Solving molecular compounds from powder diffraction data: are results always reliable?

Altomare, Angela

doi:10.1107/s2052252522006571

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…51,54 (Schlesinger et al recently report a case of four different structures that satisfactorily match an experimental XRPD pattern. 22,55 ) Nonetheless, these coincidences are rare, and powder-based indices are the natural choice when experimental XRPD patterns for the compounds of interest are available.…”

Section: Gaussian Powder-based Similarity Indexmentioning

confidence: 99%

“…Instead, we carry out a global minimization search by exploring possible deformations of structure A within a certain range, and carrying out local minimizations of G AB as a function of the lattice parameters of A. The idea of using global minimization to fit a structure to an experimental pattern has been used for the interpretation and solution of powder diffraction data, 16,[20][21][22]56 and notably it is the approach taken by FIDEL-GO. 21 Unlike FIDEL-GO, we simplify the global minimization problem so that the resulting algorithm can be used efficiently and automatically to filter candidate structures in an arbitrary CSP protocol, given experimental XRPD data about the target compound.…”

Section: Variable-cell Similarity Indexmentioning

confidence: 99%

“…This is the objective in "structure determination from powder data (SDPD)". [14][15][16][17][18][19][20][21][22] A successful application of SDPD requires expert knowledge from the user as well as a high quality pattern. In contrast, low-quality patterns (with broadened peaks because of poor crystallinity, preferred orientation effects, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Powder-Diffraction-Based Structural Comparison for Crystal Structure Prediction without Prior Indexing

Otero de la Roza

2024

Preprint

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The objective of crystal structure prediction (CSP) is to predict computationally the thermodynamically stable crystal structure of a compound from its stoichiometry or its molecular diagram. Crystal similarity indices measure the degree of similarity between two crystal structures, and are essential in CSP because they are used to identify duplicates. In addition, powder-based indices, which are based on comparing X-ray diffraction patterns, also allow the use of experimental X-ray powder diffraction data to inform the CSP search. Powder-assisted CSP presents two unique difficulties: i) the experimental and computational structures are not entirely comparable because the former is subject to thermal expansion from lattice vibrations, and ii) experimental patterns present features (noise, background contribution, varying peak shapes, etc.) that are not easily predictable computationally. In this work, we present a powder-based similarity index (GPWDF) based on a modification of de Gelder et al.'s index using cross-correlation functions that can be calculated analytically. Based on GPWDF, we also propose a variable-cell similarity index (VC-GPWDF) that assigns a high similarity score to structures that differ only by a lattice deformation and takes advantage of the analytical derivatives of GPWDF with respect to the lattice parameters. VC-GPWDF can be used to identify similarity between: two computational structures generated using different methods, a computational and a experimental structure, and two experimental structures measured under different conditions (e.g. different temperature and pressure). In addition, VC-GPWDF can also be used to compare crystal structures with experimental patterns in combination with an automatic pre-processing step. The proposed similarity indices are simple, efficient, and fully automatic. They require no indexing of the experimental pattern or a guess of the space group, account for deformations caused by varying experimental conditions, give meaningful results even when the experimental pattern is of very poor quality, and have a cost does not increase with the flexibility of the molecular motif.

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Section: Gaussian Powder-based Similarity Indexmentioning

confidence: 99%

Section: Variable-cell Similarity Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Powder-Diffraction-Based Structural Comparison for Crystal Structure Prediction without Prior Indexing

Otero de la Roza

2024

Preprint

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“…2 ) To make things more complicated, evaluation and validations of models obtained by Rietveld refinement is not straightforward either, as there are no definitive rules that distinguish one and only one correct structure solution. In particular, there are no clear‐cut criteria that determine the end of a refinement on the level of mathematical convergence using R values [26,27] or by using indicators typically found in single crystal refinement ( e. g ., ‘check cif’ alerts) [28,29] . As a least‐squares optimization, Rietveld refinement converges to the minimum with the steepest descent, which is not as deep or well‐separated from other minima as is the case for refinement against single crystal diffraction data.…”

Section: Introductionmentioning

confidence: 99%

“…This is a highly illustrative example that one can come up with multiple solutions and that ‘mere existence of a plausible crystal structure, a good Rietveld fit with a smooth difference plot, acceptable R ‐values and a successful checkCIF test does not justify the attribute ‘correct structure’.’ [27] However, with high‐quality data on crystalline samples, such ambiguities are less likely to happen.…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Small‐Molecule Crystal Structures Solved from Powder X‐Ray Diffraction Data by Using External Sources

Jung¹,

Lukin

Halász

2023

Helvetica Chimica Acta

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Structural analysis using powder X‐ray diffraction data has overcome many obstacles and nowadays is readily applicable for structural analysis of all types of compounds and materials. Being less straightforward than single crystal diffraction, it requires significant users’ input and consequently, implementation of standardized tools to assess the accuracy of crystal structures. This article discusses potential errors in crystal structure solution and refinement of small‐molecule structures obtained from PXRD data. Moreover, it proposes how accuracy of these structures can be improved by using high‐quality PXRD data, complementary external analytical techniques, knowledge stored in crystal structure databases, as well as an approach to search the parameter space to avoid local minima in testing different sets of geometry restraints.

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Powder X-ray diffraction assisted evolutionary algorithm for crystal structure prediction

Racioppi,

Otero-de-la-Roza,

Hajinazar

et al. 2025

Digital Discovery

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Solving molecular compounds from powder diffraction data: are results always reliable?

Abstract: Commentary is given on a paper [ Schlesinger et al. (2022). IUCrJ , 9 , 406–424. ] reporting on ambiguous structure determination from powder data using four different structural models of 4,11-difluoroquinacridone with similar X-ray powder patterns.

Cited by 5 publications

References 10 publications

Powder-Diffraction-Based Structural Comparison for Crystal Structure Prediction without Prior Indexing

Powder-Diffraction-Based Structural Comparison for Crystal Structure Prediction without Prior Indexing

Improving the Accuracy of Small‐Molecule Crystal Structures Solved from Powder X‐Ray Diffraction Data by Using External Sources

Powder X-ray diffraction assisted evolutionary algorithm for crystal structure prediction

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