Inverse Design of Pre‐Strain via a Predictive Electromechanical Model for Enhanced Strain Sensing

Pre‐strain engineering is widely employed to enhance the sensitivity of flexible piezoresistive sensors, yet empirical pre‐straining inevitably triggers severe trade‐offs among sensitivity, linearity, hysteresis, and effective working range, leaving the identification of the optimal pre‐strain an unresolved challenge. Here, an analytical electromechanical model rooted in microcrack evolution mechanics is developed that unifies two governing regimes — conductive‐network fragmentation during initial loading and reversible crack opening–closure during steady‐state cycling — reproducing experimental data with R 2 > 0.99 across eight pre‐strain levels. Critically, all model parameters reduce to pre‐strain‐independent material constants, leaving strain and pre‐strain as the only inputs; this separation extends the model from per‐specimen fitting to a globally calibrated predictive framework that generates the complete electromechanical response at any untested pre‐strain from only eight calibration datasets — a predictive capability absent from all prior microcrack circuit models. The predictive response surface is then coupled with multi‐objective Pareto optimization to identify the optimal pre‐strain. For smart‐tire monitoring at 10% working strain, the inversely designed sensor (43.0% pre‐strain) reduces median baseline drift from 8.52% to 1.93% ( p = 1.19 × 10 − 1 5 ), and improves the signal‐to‐noise ratio (SNR) by 68.8% ( p = 3.11 × 10 − 1 1 ) across 91 bench‐test conditions.