News Picture Generic

Combining AI and automation towards the prediction of crystal structure landscapes

May 27, 2021

Discovery of crystal structures is often a challenging and time consuming process. The combination of crystal structure prediction (CSP) with high-throughput (HT) crystallization screening methods greatly accelerates the identification and selection of structures with desirable physical properties. While CSP provides valuable insights into a molecule's structural preferences, HT rapidly evaluates hundreds of different crystallisation conditions. Such protocols have for example been used to search for polymorphs of interest. Prof. Andrew Cooper from the Materials Innovation Factory and University of Liverpool describes the state-of-the-art derived from their most recent works on the subject.

Prof. Andrew Cooper is Professor of Chemistry at the University of Liverpool, a Fellow of the Royal Society and was awarded the Hughes Medal in 2019. 

Webinar

For more information about Chemspeed solutions:

FLEX ISYNTH

ISYNTH

SWING CRYSTAL

For details please contact [email protected]

Other Recent News

Discover more news articles you might be interested in

Read more about Identifying critical powder properties for high-throughput dispensing of alumina and organic templates
News Picture 1 1 V2
Jun
16

Identifying critical powder properties for high-throughput dispensing of alumina and organic templates

Screening powder properties such as flowability, compressibility, and particle geometry is crucial for controlling ceramic processing, particularly in automated workflows that demand high reproducibility. Sacrificial templating for porous ceramics is well suited to automation because it is prone to variability arising from manual handling.

Read more about Integration of Machine Learning and Automated Synthesis for Accelerated Drug and Material Research
News Picture 1 1 V2
Jun
2

Integration of Machine Learning and Automated Synthesis for Accelerated Drug and Material Research

The challenges posed by global climate change and disease risks have intensified the demand for efficient and practical materials and molecules. Traditional trial-and-error approaches are becoming increasingly inefficient and resource-intensive. The rapid advancement of artificial intelligence (AI) has opened new avenues to accelerate research and shorten development cycles.

© Chemspeed Technologies 2026