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Approaches that combine machine learning and quantum mechanics can help us to navigate the vastness of chemical compound space. Molecules can be seen as constellations in which each star – known molecular fragments – are held together by property patterns that can guide the prediction of new molecules. See Lilienfeld et al.
One can combine two distinct chain-transfer agents to tune the dispersity of vinyl polymers. The polymer chains remain living and can be transformed into diblock copolymers.
Despite their similarity with surfactants, the mechanism by which hydrotropes assist in solubilizing apolar molecules in water is not totally understood. A mechanism is now proposed that identifies apolar interactions as driving forces of hydrotropy, in agreement with a statistical thermodynamic picture.
Although they are generally poor coupling agents under palladium catalysis, germanes are easily cross-coupled under gold catalysis, enabling a new orthogonal cross-coupling reaction.
Discovery and application of enzymes that catalyse new reactions is essential for broad implementation of biocatalysts in organic synthesis. This Review describes recent developments in biocatalytic carbon–carbon bond formation.
Machine-learning techniques have enabled, among many other applications, the exploration of molecular properties throughout chemical space. The specific development of quantum-based approaches in machine learning can now help us unravel new chemical insights.
As the International Year of the Periodic Table came to an end in 2019, the authors reflect on the chemistry and physics that drive the periodic table of the elements. This includes aspects of periodic trends, relativistic electronic-structure theory, nuclear-structure theory and the astrophysical origin of the elements.