ReactionDataExtractor 2.0: A Deep Learning Approach for Data Extraction from Chemical Reaction Schemes.

Knowledge in the chemical domain is often disseminated graphically via chemical reaction schemes. The task of describing chemical transformations is greatly simplified by introducing reaction schemes that are composed of chemical diagrams and symbols. While intuitively understood by any chemist, like most graphical representations, such drawings are not easily understood by machines; this poses a challenge in the context of data extraction.

ReactionDataExtractor: A Tool for Automated Extraction of Information from Chemical Reaction Schemes.

Chemical reaction schemes are commonly used for visual encapsulation of chemical information. Figures of reaction schemes contain chemical transformations, the chemical species involved, as well as reaction conditions. From a data-mining point of view, they constitute rich sources, densely packed with knowledge.

Refinement of pore size at sub-angstrom precision in robust metal-organic frameworks for separation of xylenes.

The demand for xylenes is projected to increase over the coming decades. The separation of xylene isomers, particularly p- and m-xylenes, is vital for the production of numerous polymers and materials. However, current state-of-the-art separation is based upon fractional crystallisation at 220 K which is highly energy intensive.