Solvent Selection Scheme Using Machine Learning Based on Physicochemical Description of Solvent Molecules: Application to Cyclic Organometallic Reaction was written by Fujinami, Mikito;Maekawara, Hiroki;Isshiki, Ryota;Seino, Junji;Yamaguchi, Junichiro;Nakai, Hiromi. And the article was included in Bulletin of the Chemical Society of Japan in 2020.Name: 2-Phenoxypyridine This article mentions the following:
A solvent selection scheme for optimization of reactions is proposed using machine learning, based on the numerical descriptions of solvent mols. Twenty-eight key solvents were represented using 17 physicochem. descriptors. Clustering anal. results implied that the descriptor represents the chem. characteristics of the solvent mols. During the assessment of an organometallic reaction system, the regression anal. indicated that learning even a small number of exptl. results can be useful for identifying solvents that will produce high exptl. yields. Observation of the regression coefficients, and both clustering and regression anal., can be effective when selecting a solvent to be used for an experiment In the experiment, the researchers used many compounds, for example, 2-Phenoxypyridine (cas: 4783-68-0Name: 2-Phenoxypyridine).
2-Phenoxypyridine (cas: 4783-68-0) belongs to pyridine derivatives. The ring atoms in the pyridine molecule are sp2-hybridized. The nitrogen is involved in the π-bonding aromatic system using its unhybridized p orbital. The lone pair is in an sp2 orbital, projecting outward from the ring in the same plane as the σ bonds. Pyridine groups exist in countless molecules, and their applications include catalysis, drug design, molecular recognition, and natural product synthesis.Name: 2-Phenoxypyridine