Superior Energy‐Storage Performance Enabled by Machine Learning Accelerated Composition Design for Lead‐Free Antiferroelectrics

ABSTRACT Development of high‐performance lead‐free AgNbO 3 (AN)‐based antiferroelectrics (AFEs) have emerged as promising candidate for high‐power energy‐storage capacitors. Routine trial‐and‐error method in enhancing energy‐storage density ( W rec ) and efficiency ( η ) encounters great challenges since extensive latent space are explored for composition screening. Using machine learning (ML) algorithms, a two‐layer stacking framework termed as SS‐PAN (stacking strategy for predicting AN‐based ceramics) is proposed here for designing high‐performance AN‐based AFEs. This framework achieves a high R 2 score of 0.82 through cross validation and outperforms individual ML model. The predicted composition represented by Li 0.01 Ag 0.99 Nb 0.5 Ta 0.5 O 3 , possesses a quasi‐linear P‐E loop and an ultrahigh W rec of 16.6 J cm −3 and η of 92.6% with an excellent figure of merit of 224.3 J cm −3 is achieved at electric field of 108 kV mm −1 in MLCC. Based on SHapley Additive exPlanations analysis, high prediction accuracy is enabled by precisely selecting features of tolerance factor and electron affinity of B ‐site element. Notably, local structure for Li 0.01 Ag 0.99 Nb 0.5 Ta 0.5 O 3 composition is thoroughly decoded by STEM and DFT calculations, where highly polar short‐range antiferroelectric nanodomains with strong localized dipole moments are induced by Li/Ta co‐doping. This work imparts a potent potential of data‐driven methodology for seeking emergent relaxor AFEs for advanced dielectric capacitor applications.