Synthesis and exceptional operational durability of polyaniline-inspired conductive ladder polymers.

Ladder-type structures can impart exceptional stability to polymeric electronic materials. This article introduces a new class of conductive polymers featuring a fully ladder-type backbone. A judicious molecular design strategy enables the synthesis of a low-defect ladder polymer, which can be efficiently oxidized and acid-doped to achieve its conductive state. The structural elucidation of this polymer and the characterization of its open-shell nature are facilitated with the assistance of studies on small molecular models. An autonomous robotic system is used to optimize the conductivity of the polymer thin film, achieving over 7 mS cm-1. Impressively, this polymer demonstrates unparalleled stability in strong acid and under harsh UV-irradiation, significantly surpassing commercial benchmarks like PEDOT:PSS and polyaniline. Moreover, it displays superior durability across numerous redox cycles as the active material in an electrochromic device and as the pseudocapacitive material in a supercapacitor device. This work provides structural design guidance for durable conductive polymers for long-term device operation.

One class classification as a practical approach for accelerating π-π co-crystal discovery.

The implementation of machine learning models has brought major changes in the decision-making process for materials design. One matter of concern for the data-driven approaches is the lack of negative data from unsuccessful synthetic attempts, which might generate inherently imbalanced datasets. We propose the application of the one-class classification methodology as an effective tool for tackling these limitations on the materials design problems. This is a concept of learning based only on a well-defined class without counter examples. An extensive study on the different one-class classification algorithms is performed until the most appropriate workflow is identified for guiding the discovery of emerging materials belonging to a relatively small class, that being the weakly bound polyaromatic hydrocarbon co-crystals. The two-step approach presented in this study first trains the model using all the known molecular combinations that form this class of co-crystals extracted from the Cambridge Structural Database (1722 molecular combinations), followed by scoring possible yet unknown pairs from the ZINC15 database (21 736 possible molecular combinations). Focusing on the highest-ranking pairs predicted to have higher probability of forming co-crystals, materials discovery can be accelerated by reducing the vast molecular space and directing the synthetic efforts of chemists. Further on, using interpretability techniques a more detailed understanding of the molecular properties causing co-crystallization is sought after. The applicability of the current methodology is demonstrated with the discovery of two novel co-crystals, namely pyrene-6H-benzo[c]chromen-6-one (1) and pyrene-9,10-dicyanoanthracene (2).