1 publication

• Published Subscription Original Research 6 May 2026

  Predicting Dielectric Constants of Polymers Using Molecular Structural Descriptors and Explainable Machine Learning: A Data-Driven Approach

  By C. Sridhathan, R. Arangasamy, Geetha Prahalad, Mohandass G., N.M.G. Kumar, M. Ram Prasad Reddy

  Abstract: Accurate prediction of dielectric constants in polymeric materials is fundamental to the rational design of advanced electronic components, energy storage capacitors, flexible substrates, and high-frequency communication circuits. Conventional approaches to identifying suitable polymer dielectrics rely on extensive experimental synthesis and characterisation, which are both time-consuming and resource-intensive. In this work, an explainable machine learning framework is developed to predict the dielectric constant of polymers directly from molecular structural descriptors derived …

  Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 297–304 Read article →