A seventh blind test of crystal structure prediction has been organized by the Cambridge
Crystallographic Data Centre. The results are presented in two parts, with this second part …

We demonstrate a machine learning-based approach which predicts the properties of crystal
structures following relaxation based on the unrelaxed structure. Use of crystal graph …

Inspired by the recent success of machine-learned interatomic potentials for crystal structure
prediction of the inorganic crystals, we present a methodology that exploits Moment Tensor …

A workflow for the digital design of crystallization processes starting from the chemical
structure of the active pharmaceutical ingredient (API) is a multistep, multidisciplinary …

A novel approach to computationally enhance the sampling of molecular crystal structures is
proposed and tested. This method is based on the use of extended variables coupled to a …

The large variety of structural morphologies realized in organic semiconductors is a big
challenge for the microscopic modeling of such systems. A global computational solution is …

A dispersion-corrected density functional theory (DFT+ D) method has been developed. It
includes a nonhybrid dispersionless generalized gradient approximation (GGA) functional …

Crystal structures that form with more than one molecule in the asymmetric unit (Z'> 1) are a
fascinating and important, if overlooked, aspect of crystal engineering. With the recent …

In recent years, significant advancements in computational methods have dramatically
enhanced the precision in determining the energetic ranking of different phases of molecular …

The seventh blind test of crystal structure prediction (CSP) methods substantially increased
the level of complexity of the target compounds relative to the previous tests organized by …