AI-designed material wrinkles enable programmable surface patterns

Jun 04, 2026

AI-guided material wrinkles could create low-cost micro- and nanoscale surface patterns for anti-counterfeiting and flexible electronics.

(Nanowerk News) Surface wrinkles, folds, and dents are usually treated as flaws, signs that a material has aged or failed. A new review argues the reverse. Researchers led by Lingnan University propose that these textures, combined with artificial intelligence (AI) assisted design, could form the basis of functional materials for anti-counterfeiting, artificial organs, and stretchable batteries. One reason is that the microscopic patterns can encode far more information than a human fingerprint.

Key Findings

The work appears as a collaborative review in Nano-Micro Letters (“Harnessing Surface Instabilities for Functional Materials: Mechanics, Morphology, and Emerging Applications”). The cross-institutional team was assembled by the Wu Jieh Yee School of Interdisciplinary Studies (WJYSIS) at Lingnan University, together with Beihang University and Northeastern University. They surveyed international progress in functional materials and set out a new design framework. A summary of morphologies, features and examples of surface instabilities: wrinkle, fold, Periodic double, ridge, crease and delamination buckle. (Image: Reproduced from DOI:10.1007/s40820-026-02162-3, CC BY) (click on image to enlarge) The central proposal recasts microscopic wrinkles, folds, and deformation patterns as functional structures rather than damage. With AI assistance, researchers could specify a desired function first, then let algorithms optimise the surface wrinkle architecture and govern how it shifts under mechanical force, heat, light, humidity, or chemical stimuli. That control could yield materials with anti-counterfeiting, information encryption, waterproofing, self-cleaning, or biomimetic biomedical functions. Because these materials combine surface structure, mechanics, and device-level design, the team sees broad application potential. Their textures hold microscale and nanoscale patterns that are extremely hard to copy, which makes them suitable as secure artificial fingerprints. Earlier studies cited by the authors found that the information density of such patterns can reach ten billion times that of a human fingerprint, sharply raising the difficulty of forgery. In biomedicine, the authors note that hydrogels and similar materials have already been shaped into folded artificial tissues, including structures that imitate brain folds, mucosal linings, and organ surfaces, pointing toward new options for artificial organs and tissue engineering. The same wrinkled architectures could also support stretchable batteries and flexible electronics, such as wearable electronic skin where sensors keep stable conductivity and sensing even under heavy deformation. Prof Chen Xi, Dean of the WJYSIS and Chair Professor of Interdisciplinary Studies at Lingnan University, said conventional methods for making microstructures, namely photolithography, mould imprinting, and laser processing, tend to require complex equipment, multiple processing steps, and rigid templates, which makes them poorly suited to soft, stretchable materials. Drawing on a material’s own mechanical behaviour alongside AI design, he argues, could produce micro- and nanostructures more efficiently, with advantages in cost and flexibility. For Chen, the broader point is conceptual rather than technical. Prof Chen said “Over the past several decades, mechanicians have devoted substantial effort to eliminating wrinkles on material surfaces. However, once we understand the underlying mechanical principles these patterns can be transformed into intelligent materials and engineered surface structures with specific functionalities. We hope this research will support researchers and engineers in Hong Kong and the Greater Bay Area in developing simpler and lower-cost methods for fabricating micro- and nanoscale surface patterns.”