Self-rectifying memristor (SRM) arrays hold tremendous potential in high-density data storage and energy-efficient neuromorphic computing. However, SRM arrays are mostly developed on rigid substrates and lack mechanical flexibility, limiting their applications in intelligent electronic skin, wearable technologies, etc. Here, we present a high-performance SRM array based on Pt/HfO₂/Ta₂O_5−x/Ti heterojunctions, which can be fabricated on a flexible polyimides (PI) substrate and demonstrates exceptional memristive performance under bending conditions (bending radius (R) = 1 cm, rectifying ratio > 10⁴, retention time > 10⁴ s and endurance > 10⁵ cycles). We demonstrate a 16 × 16 flexible memristor array offering noise filtering and data storage capabilities, which can be used to accurately process and store the signals transmitted by a pressure sensor array. This research represents an important advancement towards the realization of next-generation high-performance flexible electronics.

Constrained by the inefficiency of traditional trial-and-error methods, especially when dealing with thousands of candidate materials, the swift discovery of materials with specific properties remains a central challenge in contemporary materials research. This study employed an artificial intelligence-driven materials design framework for identifying dopants that impart antiferroelectric properties to HfO₂ materials. This strategy integrates density functional theory (DFT) with machine learning (ML) techniques to swiftly screen HfO₂ materials exhibiting stable antiferroelectric properties based on the critical electric field. This approach aims to overcome the high cost and lengthy cycles associated with traditional trial-and-error and experimental methods. Among 30 undeveloped dopants, four candidate dopants demonstrating stable antiferroelectric properties were identified. Subsequent DFT analysis highlighted the Ga dopant, which displayed favorable characteristics such as a small volume change, minimal lattice deformation, and a low critical electric field after incorporation into hafnium oxide. These findings suggest the potential for stable antiferroelectric performance. Essentially, we established a correlation between the physical characteristics of hafnium oxide dopants and their antiferroelectric performance. The approach facilitates large-scale ML predictions, rendering it applicable to a broad spectrum of functional material designs.