Adaptive global kernel interval SVR-based machine learning for accelerated dielectric constant prediction of polymer-based dielectric energy storage

Exploring the data-driven prediction strategy of dielectric constant (ε) is attractive for the rational design of polymer dielectrics with targeted property, especially for the design of high ε and low loss dielectric energy storage. To accelerate the design and discovery of novel polymer-based dielectric energy storage, the machine learning-based predictor, interval support vector regression with optimized genetic algorithm (OGA-ISVR), is proposed to predict ε values, which could improve prediction accuracy and reduce time consumption via splitting the overall data space into subspaces, then adaptively choosing the kernel function and obtaining optimal hyper-parameters by genetic algorithm in each subspace. Here, the developed model is sufficiently trained and tested from the experimentally measured data and density functional theory-based computational data at various frequencies (spanning from 60 Hz to 1015 Hz). The mapping relationships between features and property and influencing factor of ε values are identified by this machine learning-based model. Furthermore, compared with common support vector regression method, the proposed model has lower computing overhead and higher prediction accuracy. The proposed model is successfully demonstrated here for the instant property predictions of polymer dielectrics.