Crystallization of proteins is a nontrivial task, and despite the substantial efforts in robotic
automation, crystallization screening is still largely based on trial-and-error sampling of a …

The prediction of energetically stable crystal structures formed by a given chemical
composition is a central problem in solid-state physics. In principle, the crystalline state of …

High-throughput molecular biology and crystallography advances have placed an
increasing demand on crystallization, the one remaining bottleneck in macromolecular …

This Methods/Protocols article is intended for materials scientists interested in performing
machine learning-centered research. We cover broad guidelines and best practices …

Computational crystal structure prediction (CSP) methods can now be applied to the smaller
pharmaceutical molecules currently in drug development. We review the recent uses of …

Over the past three decades, the development of methods for Crystal Structure Prediction
(CSP) has primarily been curiosity-driven. Because of the obvious potential for economic …

Crystallization has been applied to a broad range of industries such as bulk and fine
chemicals and the pharmaceutical and food industries. It is important to strategically control …

The prediction of crystal structures from first-principles requires highly accurate energies for
large numbers of putative crystal structures. High accuracy of solid state density functional …

In an industrial crystallization process, crystal shape strongly influences end-product quality
and functionality, as well as downstream processing. In addition, nucleation events, solvent …

Characteristics of crystal quality, including crystal size distribution, size, morphology,
polymorphism and chirality, have been established as important indicators of crystalline …