Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem

Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry

The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent success of Deep Learning (DL) has inspired a renaissance of neural networks for their potential application in deep chemistry. DL targets direct data analysis without any human intervention. Although back-propagation NN is the main algorithm in the DL that is currently being used, unsupervised learning can be even more efficient. We review self-organizing maps (SOM) in mapping molecular representations from the 1990s to the current deep chemistry. We discovered the enormous efficiency of SOM not only for features that could be expected by humans, but also for those that are not trivial to human chemists. We reviewed the DL projects in the current literature, especially unsupervised architectures. DL appears to be efficient in pattern recognition (Deep Face) or chess (Deep Blue). However, an efficient deep chemistry is still a matter for the future. This is because the availability of measured property data in chemistry is still limited.

Comparing the Performance of Supported Ru Nanocatalysts Prepared by Chemical Reduction of RuCl3 and Thermal Decomposition of Ru3(CO)12 in the Sunlight-Powered Sabatier Reaction

The preparation of Ru nanoparticles supported on γ-Al2O3 followed by chemical reduction using RuCl3 as a precursor is demonstrated, and their properties are compared to Ru nanoparticles supported on γ-Al2O3 prepared by impregnation of γ-Al2O3 with Ru3(CO)12 and subsequent thermal decomposition. The Ru nanoparticles resulting from chemical reduction of RuCl3 are slightly larger (1.2 vs. 0.8 nm). In addition, Ru nanoparticles were deposited on Stöber SiO2 using both deposition techniques. These particles were larger than the ones deposited on γ-Al2O3 (2.5 and 3.4 nm for chemical reduction and thermal decomposition, respectively). Taking into account the size differences between the Ru nanoparticles, all catalysts display similar activity (0.14–0.63 mol·gRu−1·h−1) and selectivity (≥99%) in the sunlight-powered Sabatier reaction. Ergo, the use of toxic and volatile Ru3(CO)12 can be avoided, since catalysts prepared by chemical reduction of RuCl3 display similar catalytic performance.

CO2—A Crisis or Novel Functionalization Opportunity?

The growing emission of carbon dioxide (CO2), combined with its ecotoxicity, is the reason for the intensification of research on the new technology of CO2 management. Currently, it is believed that it is not possible to eliminate whole CO2 emissions. However, a sustainable balance sheet is possible. The solution is technologies that use carbon dioxide as a raw material. Many of these methods are based on CO2 methanation, for example, projects such as Power-to-Gas, production of fuels, or polymers. This article presents the concept of using CO2 as a raw material, the catalytic conversion of carbon dioxide to methane, and consideration on CO2 methanation catalysts and their design.

The Physical Chemistry and Chemical Physics (PCCP) Section of the International Journal of Molecular Sciences in Its Publications: The First 300 Thematic Articles in the First 3 Years

The Physical Chemistry and Chemical Physics Section (PCCP Section) is one of the youngest among the sections of the International Journal of Molecular Sciences (IJMS)—the year 2021 will only mark three years since its inception [...]