Artificial intelligence and specifically machine learning applications are nowadays used in a
variety of scientific applications and cutting-edge technologies, where they have a …

Organic molecules tend to close pack to form dense structures when they are crystallised
from organic solvents. Porous molecular crystals defy this rule: they contain open space …

Predicting and controlling crystallinity of molecular materials has applications in a crystal
engineering context, as well as process control and formulation in the pharmaceutical …

This chapter addresses the role that data‐driven approaches, especially machine learning
methods, are expected to play in materials research in the immediate future. Machine …

The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled
roughly half a million annotated images of macromolecular crystallization experiments from …

We demonstrate the application of deep neural networks as a machine-learning tool for the
analysis of a large collection of crystallographic data contained in the crystal structure …

With the advent of faster computer processors and especially graphics processing units
(GPUs) over the last few decades, the use of data-intensive machine learning (ML) and …

The development of a robust manufacturing process for solid organic materials, such as
pharmaceuticals, can be complicated when the molecules crystallize in different solid forms …

The combination of modern machine learning (ML) approaches with high-quality data from
quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost …

Predictions of relative stabilities of (competing) molecular crystals are of great technological
relevance, most notably for the pharmaceutical industry. However, they present a long …