AI Model for Accurate Prediction of Dielectric Constants in Materials

Predicting dielectric constants of crystalline materials using explainable machine learning and composition-aware feature engineering

An explainable machine-learning framework was developed for dielectric constant prediction using 52,168 crystalline materials extracted from the Joint Automated Repository for Various Integrated Simulations (JARVIS-DFT) database, demonstrating the complementary roles of electronic structure and elemental chemistry.

Interpretable Machine Learning Prediction of the Dielectric Constant and Bandgap of Polymers for Flexible Electronics

Polymer dielectrics are central to flexible and printed electronics, where a material must combine a sufficiently high dielectric constant with a wide electronic bandgap to suppress leakage. Experimental or first-principles screening is slow, motivating data-driven surrogates. Here we develop an interpretable machine learning workflow that predicts both the total dielectric constant and the HSE bandgap of polymer repeat units directly from a monomer structure, using an open density-functional-theory dataset of 284 four-block polymers. Polymers are encoded with 217 RDKit descriptors and a 1024-bit Morgan fingerprint; four regressors are benchmarked under nested five-fold cross-validation with paired significance testing. The bandgap reaches R2=0.83±0.04 (MAE =0.35 eV) and the total dielectric constant R2=0.64±0.06 (MAE =0.44), but paired tests find the models statistically indistinguishable for the bandgap. Decomposing the permittivity explains its lower ceiling: the ionic component is only 15% of the magnitude yet carries 28% of the squared error, and learning curves confirm a representational rather than a data-quantity limit. Read through the Penn relation, the SHAP descriptors yield an explicit design rule—raise permittivity with polar, non-conjugated motifs rather than extended conjugation. Screening 571 unseen candidates with bootstrap uncertainties, an applicability domain and a threshold sensitivity analysis nominates carbamate/urea-type wide-bandgap high-k repeat units.

Machine learning-assisted first-principles investigation of structural, electronic, and photovoltaic properties of perovskite materials

A unified multiscale system that entails the implementation of density functional theory (DFT), machine learning (ML), and device-level simulation to hasten the search and development of high-performance perovskite solar cell materials is presented.

Surrogate Modelling Approach in Materials Dielectric Constant Prediction: Sim-to-Real and Generalization Capability Analysis

The Use of Machine Learning Models for Predicting the Dielectric Strength of Gases

Technological advancements in high voltage systems have pushed sulfur hexafluoride (SF6) to its operational limits. Furthermore, this gas has other drawbacks including a high liquefaction temperature and a high global warming potential. Therefore, there has been an urgent need to find alternative gases with high dielectric strength (DS). In this work, density functional theory (DFT) is used to calculate molecular descriptors that are fed into an artificial neural network (ANN) and a random forest (RF). These machine learning (ML) models are then used to predict the DS for hundreds of molecules. A finite element model (FEM) is also used to calculate the electric field profile of multiple simple electrode geometries as the applied voltage to the system is increased. Results indicate that the random forest model has better generalization to unseen data than the neural network. The highest DS value predicted by the RF was 2.16 relative to the experimental DS of SF6. The results also demonstrate how choosing a gas with a higher DS and a geometry with minimal edges and corners can significantly increase the operating voltage of an electrical system. Due to its superior generalization, the RF represents the most promising path toward an accurate DS predictor once sufficient experimental data are available.

Machine Learning-Guided Electronic Configuration Design for Dielectrics With High Energy Storage Performance.

Dielectric capacitors possess ultrahigh power density and are promising for advanced energy storage and pulsed power applications, yet conventional trial-and-error doping strategies limit design efficiency and performance optimization. Here, an interpretable machine learning framework based on Shapley Additive exPlanations is developed to guide the compositional design of K0.5Na0.5NbO3-based relaxor ferroelectrics. Key descriptors governing polarization behavior are identified from 18 features, leading to Sc as the optimal dopant, outperforming the Bi5/6In0.5Sn0.5O3-doped system. The designed 0.85(0.96K0.48Na0.52NbO3-0.04BaZrO3)-0.15Bi5/6Sc0.5Sn0.5O3 is predicted to exhibit a large polarization difference of 26.3 µC cm-2. First-principles calculations and experimental validation show that Sc3+ forms more ionic Sc-O bond than In3+, giving less distorted and markedly more rigid polar units. These rigid units act as a restoring force during polarization switching and suppress the remnant polarization, while the strong Sc─O bond reduces oxygen vacancy concentration and enhances the dielectric breakdown strength. As a result, an ultrahigh energy storage density of 6.91 J cm-3 and efficiency of 91.9% are achieved, representing a 66.1% improvement over the In-based system. This work demonstrates an interpretable machine learning-guided electronic configuration strategy and highlights the critical role of dopant electronic structure and local bonding in optimizing energy storage performance.