Machine learning‐assisted optimization of MoTe 2 neural electrode for enhanced electrochemical performance

Abstract Neural electrodes serve as the bidirectional bridge between the nervous system and external devices and play an irreplaceable role in the diagnosis and treatment of neurological disorders. However, conventional electrodes often suffer from high impedance, limited charge‐storage capacity, and poor stability, which constrain their durability. Herein, we coupled a machine learning‐assisted strategy with a multipotential‐step method to optimize the deposition of MoTe 2 quantum dots (QDs) on bare electrodes. After preliminary experimental screening to define the parameter ranges, the surrogate model mapping deposition parameters to overall electrochemical performance score was established and cross‐validated. Prediction over the full parameter space yielded an optimal combination (0.5 mg mL −1 , 0.2 V, 2 s, and 3 layers) that coincided with the experimentally screened optimum, and uncertainty analysis indicated its robustness. Relative to bare electrodes, the resulting MoTe 2 electrodes showed a 6.8‐fold lower impedance at 1 kHz together with 5.8‐ and 9.2‐fold higher charge‐storage capacity and double‐layer capacitance. The coating retained stable electrochemical behavior after 180° bending, repeated cycling, ultrasonic agitation, and 7‐week immersion, indicating good adhesion and durability. Together, these improved electrochemical and mechanical‐durability properties highlight MoTe 2 QDs as a promising coating material for next‐generation neural interfaces.