Application of Patterson-function direct methods to materials characterization

PhAI: A deep-learning approach to solve the crystallographic phase problem

X-ray crystallography provides a distinctive view on the three-dimensional structure of crystals. To reconstruct the electron density map, the complex structure factors [Formula: see text] of a sufficiently large number of diffracted reflections must be known. In a conventional experiment, only the amplitudes [Formula: see text] are obtained, and the phases ϕ are lost. This is the crystallographic phase problem. In this work, we show that a neural network, trained on millions of artificial structure data, can solve the phase problem at a resolution of only 2 angstroms, using only 10 to 20% of the data needed for direct methods. The network works in common space groups and for modest unit-cell dimensions and suggests that neural networks could be used to solve the phase problem in the general case for weakly scattering crystals.

Increasing data completeness in synchrotron tts-microdiffraction experiments for δ-recycling phasing of low-symmetry compounds

Successful phasing of synchrotron through-the-substrate microdiffraction data by

Recent developments in the structural science of materials

This Editorial surveys the current status and recent developments in the structural science of materials as exemplified by the articles recently published in IUCrJ.

Application of synchrotron through-the-substrate microdiffraction to crystals in polished thin sections

The synchrotron through-the-substrate X-ray microdiffraction technique (tts-μXRD) is extended to the structural study of microvolumes of crystals embedded in polished thin sections of compact materials [Rius, Labrador, Crespi, Frontera, Vallcorba & Melgarejo (2011 ▸). J.Synchrotron Rad. 18, 891-898]. The resulting tts-μXRD procedure includes some basic steps: (i) collection of a limited number of consecutive two-dimensional patterns (frames) for each randomly oriented crystal microvolume; (ii) refinement of the metric from the one-dimensional diffraction pattern which results from circularly averaging the sum of collected frames; (iii) determination of the reciprocal lattice orientation of each randomly oriented crystal microvolume which allows assigning the hkl indices to the spots and, consequently, merging the intensities of the different frames into a single-crystal data set (frame merging); and (iv) merging of the individual crystal data sets (multicrystal merging) to produce an extended data set suitable for structure refinement/solution. Its viability for crystal structure solution by Patterson function direct methods (δ recycling) and for accurate single-crystal least-squares refinements is demonstrated with some representative examples from petrology in which different glass substrate thicknesses have been employed. The section of the crystal microvolume must be at least of the same order of magnitude as the focus of the beam (15 × 15 µm in the provided examples). Thanks to its versatility and experimental simplicity, this method-ology should be useful for disciplines as disparate as petrology, materials science and cultural heritage.