This paper addresses a review of platinum-based hydrosilylation catalysts. The main field of application of these catalysts is the curing of silicone polymers. Since the 1960s, this area has developed rapidly in connection with the emergence of new polymer compositions and new areas of application. Here we describe general mechanisms of the catalyst activity and the structural effects of the ligands on activity and stability of the catalysts together with the methods for their synthesis.

Machine learning can make a strong contribution to accelerating the discovery of transition metal complexes (TMC). These compounds will play a key role in the development of new technologies for...

Modification of physical properties of materials and design of materials with on-demand characteristics is at the heart of modern technology. Rare application relies on pure materials—most devices and technologies require careful design of materials properties through alloying, creating heterostructures of composites, or controllable introduction of defects. At the same time, such designer materials are notoriously difficult to model. Thus, it is very tempting to apply machine learning methods to such systems. Unfortunately, there is only a handful of machine learning-friendly material databases available these days. We develop a platform for easy implementation of machine learning techniques to materials design and populate it with datasets on pristine and defected materials. Here we introduce the 2D Material Defect (2DMD) datasets that include defect properties of represented 2D materials such as MoS2, WSe2, hBN, GaSe, InSe, and black phosphorous, calculated using DFT. Our study provides a data-driven physical understanding of complex behaviors of defect properties in 2D materials, holding promise for a guide to the development of efficient machine learning models. In addition, with the increasing enrollment of datasets, our database could provide a platform for designing materials with predetermined properties.

No abstract