Crystal-Site-Based Artificial Neural Networks for Material Classification

In materials science, crystal structures are the cornerstone in the structure–property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the different atomic environments in a crystal compound can be constructed as input data for artificial neural networks (ANNs). In this article, we show the performance of a series of ANNs developed using crystal-site-based features. These ANNs were developed to classify compounds into halite, garnet, fluorite, hexagonal perovskite, ilmenite, layered perovskite, -o-tp- perovskite, perovskite, and spinel structures. Using crystal-site-based features, the ANNs were able to classify the crystal compounds with a 93.72% average precision. Furthermore, the ANNs were able to retrieve missing compounds with one of these archetypical structure types from a database. Finally, we showed that the developed ANNs were also suitable for a multitask learning paradigm, since the extracted information in the hidden layers linearly correlated with lattice parameters of the crystal structures.

Mesophase behaviour of a cyanobiphenyl molecule in polar aprotic solvent: Rigidity effect

In this paper, a theoretical study has been carried out on a liquid crystal compound named p-n-propyl cyanobiphenyl (3CB). The different modes of interaction energy values in a polar aprotic solvent (ethyl acetate) for small amount of translation and rotation are calculated. The corresponding probabilities have been calculated at both room temperature (300 K) and transition temperature (303.3 K). The rigidity parameter for stacking and in-plane interactions has been estimated and then the stability of molecule according to probability and rigidity at definite translation and rotation has been concluded. The change in the characteristics and stability of the compound at transition temperature has been observed. The dependence of mesophase behaviour with change in the certain configurations and orientation of the molecules have been discussed. These observed results provide an insight about the process of mesophase structure and its formation. The present compound may guide in establishing the other molecular models with transition temperature nearer to room temperature.

Microfluidic electrochemistry for single-electron transfer redox-neutral reactions

Electrochemistry offers opportunities to promote single-electron transfer (SET) redox-neutral chemistries similar to those recently discovered using visible-light photocatalysis but without the use of an expensive photocatalyst. Herein, we introduce a microfluidic redox-neutral electrochemistry (μRN-eChem) platform that has broad applicability to SET chemistry, including radical-radical cross-coupling, Minisci-type reactions, and nickel-catalyzed C(sp2)–O cross-coupling. The cathode and anode simultaneously generate the corresponding reactive intermediates, and selective transformation is facilitated by the rapid molecular diffusion across a microfluidic channel that outpaces the decomposition of the intermediates. μRN-eChem was shown to enable a two-step gram-scale electrosynthesis of a nematic liquid crystal compound, demonstrating its practicality.