Language models exhibit a profound aptitude for addressing multimodal and multidomain challenges, a competency that eludes the majority of off-the-shelf machine learning models. Consequently, language models hold great potential for comprehending the intricate interplay between material compositions and diverse properties, thereby accelerating material design, particularly in the realm of polymers. While past limitations in polymer data hindered the use of data-intensive language models, the growing availability of standardized polymer data and effective data augmentation techniques now opens doors to previously uncharted territories. Here, we present a revolutionary model to enable rapid and precise prediction of Polymer properties via the power of Natural language and Chemical language (PolyNC). To showcase the efficacy of PolyNC, we have meticulously curated a labeled prompt-structure-property corpus encompassing 22,970 polymer data points on a series of essential polymer properties. Through the use of natural language prompts, PolyNC gains a comprehensive understanding of polymer properties, while employing chemical language (SMILES) to describe polymer structures. In a unified text-to-text manner, PolyNC consistently demonstrates exceptional performance on both regression tasks (such as property prediction) and the classification task (polymer classification). Simultaneous and interactive multitask learning enables PolyNC to holistically grasp the structure-property relationships of polymers. Through a combination of experiments and characterizations, the generalization ability of PolyNC has been demonstrated, with attention analysis further indicating that PolyNC effectively learns structural information about polymers from multimodal Inputs. This work provides compelling evidence of the potential for deploying end-to-end language models in polymer research, representing a significant advancement in the AI community's dedicated pursuit of advancing polymer science.

Models were obtained to predict the T _g of polyimides and compared with the experimental results. 110 polyimides are screened through high-throughput calculations and chemical knowledge. Novel molecular design guidelines are provided.

Low-dielectric polyimide (PI) nanofoams were prepared by introducing nanopores into the PI matrix containing fluorine groups. The nanopores were formed by thermolysis of the thermally labile content, namely, polyethylene glycol (PEG) oligomers, in air. The prepared PI nanofoams were characterized by Fourier transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM), thermogravimetric analysis (TGA), dynamic mechanical analysis (DMA), and broadband dielectric spectroscopy. Results indicated that the PI nanofoams showed nanosized closed pores, excellent thermal stability, and a low dielectric constant of 2.12. The dielectric constant of the as-prepared nanofoams was stable within −150 °C to 150 °C. The thermal decomposition process of PEG in the PI matrix was designed and optimized to control the decomposition rate of PEG and the diffusion rate of the decomposition products of PEG. The dielectric constant of the nanofoams significantly decreased from 2.45 to 2.12 as the heating rate decreased from 5 °C min⁻¹ to 1 °C min⁻¹. The as-prepared PI nanofoams exhibited excellent properties and thus could be used in the microelectronics industry as a dielectric layer, multi-chip modules, or integrated circuit chips.