Crystallization plays a crucial role in defining the quality and functionality of products across
various industries, including pharmaceutical, food and beverage, and chemical …

Crystals are integral to a variety of industrial applications, such as the development of
pharmaceuticals and advancements in material science. To anticipate crystal behavior and …

In this work, a combination of a Convolutional Neural Network (CNN) based measurement
sensor and a reinforcement learning (RL) framework that speeds up the control loop is …

The ever-increasing demand for novel drug development has spurred the adaptation of
conventional research methods in the era of artificial intelligence. Pharmaceutical …

The crystal morphology of active pharmaceutical ingredients is a key attribute for product
design, manufacturing and pharmacological performance. Currently, the morphology of …

Manual crystallization trials have historically posed significant challenges, demanding
substantial expertise for process development and often offering unpredictable outcomes …

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

In crystallization processes, the control of particle size distribution, shape and purity are
crucial to achieve the targeted critical quality attributes of the final drug product and meet the …

In the current report, three machine learning approaches were assessed for their ability to
predict the crystallization propensities of a set of small organic compounds (< 709 Da). The …

Driven by the increasing demands of producing consistent and high-quality crystals for high
value-added products such as pharmaceutical ingredients, the operation and design of a …