Accelerating the Discovery of Low-Dielectric Polyimides Based on Interpretable Machine Learning

Rapidly advancing computer technology has demonstrated great potential in the development of promising materials. Polyimide (PI), an important dielectric material, is widely applied in modern industries. The demand for devices with low dielectric and low energy consumption in the microelectronics industry has led to a growing need for low dielectric constant (Dk) PI. Developing Dk prediction models using machine learning (ML) algorithms can help construct quantitative structure–property relationships and deeply understand the molecular nature that affects the dielectric properties of PI. In this study, we assembled a data set comprising 970 PIs and extracted key structural features using RDKit. Among the 12 popular ML algorithms, the Extra Trees-based model yielded the most accurate results, achieving a coefficient of determination of 0.897 for the test set, a mean absolute error of 0.194, and a root-mean-square error of 0.267. SHapley Additive exPlanation analysis was then employed to explain the optimal model for Dk prediction from a physicochemical point of view and structural aspects. The study identified the BCUT2D_LOGPHI descriptor as being particularly influential on Dk, showing a negative correlation. More importantly, eight potential PI candidates with low Dk values were designed according to the chemical insights of the key descriptors, which were verified through all-atom molecular dynamics (MD) simulation. By comparison of the calculated and predicted Dk values, the lowest prediction deviation was found to be approximately 1.06%. The proposed methodology achieved a prediction accuracy comparable to that of traditional MD simulation, but the computational time and resource consumption were dramatically reduced. And Schuffenhauer’s synthetic accessibility scores were used to evaluate the ease of synthesis of each PI before the experiment. This research demonstrates the feasibility of using ML methods to accelerate the property prediction and molecular design and provides a powerful tool for discovering high-performance dielectric materials in the future.