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Although anion‐exchange membranes (AEMs) are commonly used in fuel cells and water electrolyzers, their widespread commercialization is hindered by problems such as low anion conductivity and durability. Moreover, the development of high‐performance AEMs remains complex and time consuming. Here, we address these challenges by proposing an innovative approach for the efficient design and screening of AEM polymers using unsupervised machine learning. Our model, which combines principal component analysis with uniform manifold approximation and projection, generates an intuitive map that clusters AEM polymers based on structural similarities without any predefined knowledge regarding anion conductivity or other experimentally derived variables. As a powerful navigation tool, this map provides insights into promising main‐chain structures, such as poly(arylene alkylene)s with consistently high conductivity and polyolefins with exceptional performance depending on the substituent. Furthermore, assisted by key molecular descriptors, inverse analysis with this model allows targeted design and property prediction before synthesis, which will significantly accelerate the discovery of novel AEM polymers. This work represents a paradigm shift not only in AEM research but also generally in materials research, moving from black‐box predictions toward interpretable guidelines that foster collaboration between researchers and machine learning for efficient and informed material development. [ABSTRACT FROM AUTHOR]

This document is a discussion on the topic of organic thermoelectrics, covering various aspects such as optimizing properties and efficiency, effects of side-chain lengths, and the use of materials in organic electrochemical transistors. The participants provide insights and explanations based on their research and experiments, discussing topics related to synthesis, properties, and potential applications of different materials. The text also addresses the giant Seebeck effect in organic semiconductors, low electrical conductivity in thermoelectric materials, and the thermal stability of the materials. The study explores the use of different materials and solvents in the doping process and discusses the mechanism of proton-coupled electron transfer. [Extracted from the article]

Single-walled carbon nanotubes (SWCNTs) are promising thermoelectric materials because of their high electrical conductivity and Seebeck coefficient. To construct a thermoelectric generator (TEG) employing SWCNTs, the development of n-type regions within a single piece of p-type SWCNTs has gained interest, offering a seamless TEG structure without the need for metal electrodes to bridge the p- and n-type regions. Several patterning methods such as thermal deposition, plasma-induced degradation, and photoinduced doping have been put forward as potential substitutes for traditional drop casting owing to their straightforward processes and superior patterning resolution. In this study, a photobase generator (PBG) was utilized as an n-type dopant for photoinduced doping. The PBG-doped SWCNTs demonstrated exceptional thermal stability, retaining a p-type nature in the absence of UV irradiation, which, upon UV exposure, spontaneously transitioned to n-type. Moreover, the resulting n-doped region displayed remarkable stability in air for more than 100 days. Employing this technique, planar-type TEG devices with up to six p- and n-type regions in sequence were constructed. The TEGs exhibited an in-plane open-circuit voltage and maximum power output of 3.45 mV and 5.75 nW, respectively, when a temperature gradient of 30 °C was applied between the front and back sides of the sheets. [ABSTRACT FROM AUTHOR]