The seventh blind test of crystal structure prediction: structure generation methods.

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern.

Ultrafast Yttrium Hydride Chemistry at High Pressures via Non-equilibrium States Induced by an X-ray Free Electron Laser.

Controlling the formation and stoichiometric content of the desired phases of materials has become of central interest for a variety of fields. The possibility of accessing metastable states by initiating reactions by X-ray-triggered mechanisms over ultrashort time scales has been enabled by the development of X-ray free electron lasers (XFELs). Utilizing the exceptionally high-brilliance X-ray pulses from the EuXFEL, we report the synthesis of a previously unobserved yttrium hydride under high pressure, along with nonstoichiometric changes in hydrogen content as probed at a repetition rate of 4.

Beyond theory-driven discovery: introducing hot random search and datum-derived structures.

Data-driven methods have transformed the prospects of the computational chemical sciences, with machine-learned interatomic potentials (MLIPs) speeding up calculations by several orders of magnitude. I reflect on theory-driven, as opposed to data-driven, discovery based on random structure searching (AIRSS), and then introduce two new methods that exploit machine-learning acceleration. I show how long high-throughput anneals, between direct structural relaxation, enabled by ephemeral data-derived potentials (EDDPs), can be incorporated into AIRSS to bias the sampling of challenging systems towards low-energy configurations.