Democratisation of dynamical 3D ED: structure analysis using dynamical diffraction applied to all types of 3D electron diffraction data 

Session

Stream 2: EMAG - Electron Crystallography and Diffraction

Authors

Dr. Paul Klar (1), Dr. Lukas Palatinus (1)

Affiliations

1. Institute of Physics of the CAS

Keywords

3D ED

MicroED

nanocrystals

structure analysis

Abstract text

The crystal structure determination from sub-micrometric samples by means of 3D electron diffraction (3D ED) is widely applied to all classes of crystalline materials like minerals, pharmaceuticals or catalysts [1]. The diffraction data for the analysis can be collected by a number of different techniques [2]. All of them share the basic principle of rotating the crystal around the goniometer axis and recording diffraction at various orientations of the crystal. The techniques differ in the exact way the diffraction patterns are recorded. The patterns can either be stationary (ADT, RED), they can be collected during the crystal rotation (cRED, IEDT, MicroED) or beam precession can be applied during the exposure.

Regardless of the technique, crystal structures can be solved and refined from the data. However, most refinements use the kinematical approximation, which ignores the dynamical diffraction effects present in all electron diffraction data. However, the key to accurate structural information lies in these dynamical effects, and a method called dynamical refinement, which includes these effects in the structure refinement process, provided some of the most accurate structure models. So far, dynamical refinement exclusively required 3D ED data recorded with beam precession [3]. The limitation to this particular technique restricted the applicability of the method.

Recently, the method of dynamical refinement was generalised so that it can be applied to all static and continuous-rotation 3D ED geometries like ADT, cRED, and MicroED, making it applicable in any electron crystallography laboratory [4]. The method was applied to nine compounds, ranging from inorganic compounds and minerals (quartz, natrolite, mordenite, cobalt aluminum phosphate), through small molecules (glycine, carbamazepine, limaspermidine) to relatively large molecules abiraterone acetate and methylene blue derivative MBBF4. The dynamical refinement was compared with the kinematical refinement. The results demonstrate clearly that the dynamical refinement achieves significantly better results in many aspects, including:

• improved accuracy of the atomic positions and bond lengths
• up to two times lower crystallographic figures of merit like R(obs) or wR(all)
• up to four-fold reduction of the noise in the maps of difference electrostatic potential
• improved and more reliable detection of weak scatterers like hydrogen atoms and partially occupied atomic positions
• easy, fast and robust determination of the absolute structure, with the reliability of the determination being independent of the chemical composition of the crystal. 

The procedure is compatible with all available 3D ED data collection methods, and it thus makes accurate structure analysis accessible to a broad range of electron crystallography laboratories. As such this work allows to further close the gap in the quality of structure models between 3D ED- and XRD-based structure determinations. Nevertheless, this gap is evidently not fully closed yet, as R factors obtained from the dynamical refinement are typically close to 10% and rarely below 7%, while values below 4% are common for x-ray diffraction data. The new version of the dynamical refinement method pushes the limits of the 3D ED-based structure analysis and, at the same time, forms a basis for further investigation of the accuracy of structure determination and for further improvement of the quality of structure analysis from 3D ED data.

References

[1] Gemmi, M. et al., ACS Central Science 5, 1315–1329 (2019)

[2] Gemmi, M. and Lanza, A., Acta Crystallogr. B 75, 495-504 (2019)

[3] Palatinus, L., Petříček, V. & Corrêa, C. A., Acta Crystallogr. A 71, 235–244 (2015)

[4] Klar, P., Krysiak, Y., Xu, H., Zou, X.-D., Palatinus, L., submitted (2021)