Pendant Group Functionalization of Cyclic Olefin for High Temperature and High-Density Energy Storage.

High-temperature flexible polymer dielectrics are critical for high density energy storage and conversion. The need to simultaneously possess a high bandgap, dielectric constant and glass transition temperature forms a substantial design challenge for novel dielectric polymers. Here, by varying halogen substituents of an aromatic pendant hanging off a bicyclic mainchain polymer, a class of high-temperature olefins with adjustable thermal stability are obtained, all with uncompromised large bandgaps. Halogens substitution of the pendant groups at para or ortho position of polyoxanorborneneimides (PONB) imparts it with tunable high glass transition from 220 to 245 °C, while with high breakdown strength of 625-800 MV/m. A high energy density of 7.1 J/cc at 200 °C is achieved with p-POClNB, representing the highest energy density reported among homo-polymers. Molecular dynamic simulations and ultrafast infrared spectroscopy are used to probe the free volume element distribution and chain relaxations pertinent to dielectric thermal properties. An increase in free volume element is observed with the change in the pendant group from fluorine to bromine at the para position; however, smaller free volume element is observed for the same pendant when at the ortho position due to steric hindrance. With the dielectric constant and bandgap remaining stable, properly designing the pendant groups of PONB boosts its thermal stability for high density electrification.

Effect of Fluorine in Redesigning Energy-Storage Properties of High-Temperature Dielectric Polymers.

Exploration of novel polymer dielectrics exhibiting high electric-field stability and high energy density with high efficiency at elevated temperatures is urgently needed for ever-demanding energy-storage technologies. Conventional high-temperature polymers with conjugated backbone structures cannot fulfill this demand due to their deteriorated performance at elevated electric fields. Here, in search of new polymer structures, we have explored the effect of fluorine groups on the energy-storage properties of polyoxanorbornene imide polymers with simultaneous wide band gap and high glass transition temperature (Tg). The systematic synthesis of polymers with varying amounts of fluorine is carried out and characterized for the energy-storage properties. The incorporation of fluorine imparts flexibility to the polymer structure, and free-standing films can be obtained. An oxanorbornene copolymer with 25% fluorination exhibits a high breakdown strength of 700 MV/m and a discharged energy density of 6.3 J/cm3 with 90% efficiency. The incorporation of fluorine helps to increase the polymer band gap, as observed using UV-vis spectroscopy, but lowers the polymer Tg, as shown by differential scanning calorimetry. Both the displacement-electric field (D-E) hysteresis loop and high-field conduction measurements show increased conduction loss for polymers with higher fluorine content, despite their larger band gap. The presence of excess free volume may play a key role in increasing the conduction current and lowering the efficiency of polymers with high fluorine content. Such an improved understanding of the effect of fluorination on the polymer energy-storage properties, as revealed in this systematic molecular engineering study, broadens the basis of material-informatic proxies to enable a more targeted codesign of scalable and efficient polymer dielectrics.

Polymer Informatics at Scale with Multitask Graph Neural Networks.

Artificial intelligence-based methods are becoming increasingly effective at screening libraries of polymers down to a selection that is manageable for experimental inquiry. The vast majority of presently adopted approaches for polymer screening rely on handcrafted chemostructural features extracted from polymer repeat units-a burdensome task as polymer libraries, which approximate the polymer chemical search space, progressively grow over time. Here, we demonstrate that directly "machine learning" important features from a polymer repeat unit is a cheap and viable alternative to extracting expensive features by hand. Our approach-based on graph neural networks, multitask learning, and other advanced deep learning techniques-speeds up feature extraction by 1-2 orders of magnitude relative to presently adopted handcrafted methods without compromising model accuracy for a variety of polymer property prediction tasks. We anticipate that our approach, which unlocks the screening of truly massive polymer libraries at scale, will enable more sophisticated and large scale screening technologies in the field of polymer informatics.