Machine-Learning-Enabled Framework in Engineering Plastics Discovery: A Case Study of Designing Polyimides with Desired Glass-Transition Temperature.

Great and continuous efforts have been made to discover high-performance engineering plastics with specific properties to replace traditional engineering materials in many fields. The utilization of machine learning (ML) has brought more opportunities for the discovery of high-performing engineering plastics. However, hindered by either the relatively small database or a lack of accurate structure descriptors with clear physical and chemical meanings relating to polymer properties, the current ML studies show some flaws in the accuracy and efficiency in polymer development. Herein, we collected a dataset of 878 polyimides (PI), one of the best engineering plastics, with experimentally measured glass-transition temperature (Tg) values, and developed a rapid and accurate ML approach to design PI candidates with the desired Tg value. After the conversion from PI structures into "mechanically identifiable" SMILES (Simplified molecular input line entry system) language, the eight most critical descriptors were ultimately obtained by multiple analysis methods. The physiochemical meaning of the key descriptors was further analyzed carefully to translate the implicit "machine language" to chemical knowledge. The artificial neural network (ANN)-based model gave the most accurate results with a root-mean-square error of ∼11 K among the studied ML methods. More importantly, three potential PI candidates with desired Tg (DPIs) were designed according to the chemical insight of the key descriptors, which were then verified by experiments. The experimental and predicted Tg values of DPIs have an acceptable average deviation of ca. 3.66%. This accuracy has reached the level of the traditional molecular simulation, but the time consumption and hold-up computing resource are tremendously reduced. Furthermore, the current ML approach could offer a scalable and adaptable framework in future engineer plastics innovation.

Direct Asymmetric Vinylogous Mannich Addition of 3,5-Disubstituted-4-nitroisoxazoles to Isatin-Derived Imines Catalyzed by a Bifunctional Phase-Transfer-Catalyst.

Asymmetric Mannich-type addition of 3,5-disubstituted-4-nitroisoxazoles to isatin-derived Boc-protected imines has been realized by using 0.01 equiv of amide phosphonium salt as a phase transfer catalyst. Our current methodology allows for the formation of 3-isoxazolylmethyl-substituted 3-aminooxindoles in excellent yields with good to excellent enantioselectivities. The practical value of this methodology was exemplified by a gram-scale synthesis of 5an, a key intermediate for the formal synthesis (+)-AG-041R.

Asymmetric Catalytic [4 + 2] Cycloaddition via Cu-Allenylidene Intermediate: Stereoselective Synthesis of Tetrahydroquinolines Fused with a γ-Lactone Moiety.

A decarboxylative formal [4 + 2] cycloaddition reaction between ethynyl benzoxazinanones and 5-substituted 2-silyloxyfurans catalyzed by chiral Cu-Pybox complex is described. This method allows the formation of intriguing tetrahydroquinolines fused with a butyrolactone moiety featuring three contiguous chiral centers in high yields with excellent diastereo- and enantioselectivities in most cases. The utility of this method was exemplified by the removal of the N-protecting groups and derivatization on the terminal alkyne functionality of the cyclization products.