ArticleProceedings of the National Academy of Sciences of the United States of America2024
Data-driven inverse design of flexible pressure sensors.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Customizing Tactile Sensors via Machine Learning-Driven Inverse Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Super-Capacitive Pressure Sensor with Ultrahigh Sensitivity and Wide Linear Pressure Sensing Range for Human Bio-Signal Detection and Electronic Skin.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Machine learning-enabled inverse design of bioinspired layered composite structures with maximum auxetic performance.Communications engineering · 2025Article
- Impact of Microstructure on Sensing Performance of Fiber-Based Iontronic Pressure Sensors: A Comparative Study.Sensors (Basel, Switzerland) · 2025Article
- Skin-Inspired Ultra-Linear Flexible Iontronic Pressure Sensors for Wearable Musculoskeletal Monitoring.Nano-micro letters · 2025Article
- High-Conductivity, Self-Healing, and Adhesive Ionic Hydrogels for Health Monitoring and Human-Machine Interactions Under Extreme Cold Conditions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Design, Fabrication, and Application of Large-Area Flexible Pressure and Strain Sensor Arrays: A Review.Micromachines · 2025Review
- Advanced Morphological and Material Engineering for High-Performance Interfacial Iontronic Pressure Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Non-hygroscopic ionogel-based humidity-insensitive iontronic sensor arrays for intra-articular pressure sensing.National science review · 2024Article
- Data-driven inverse design of flexible pressure sensors.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Artificial skins or flexible pressure sensors that mimic human cutaneous mechanoreceptors transduce tactile stimuli to quantitative electrical signals. Conventional trial-and-error designs for such devices follow a forward structure-to-property routine, which is usually time-consuming and determines one possible solution in one run. Data-driven inverse design can precisely target desired functions while showing far higher productivity, however, it is still absent for flexible pressure sensors because of the difficulties in acquiring a large amount of data. Here, we report a property-to-structure inverse design of flexible pressure sensors, exhibiting a significantly greater efficiency than the conventional routine. We use a reduced-order model that analytically constrains the design scope and an iterative "jumping-selection" method together with a surrogate model that enhances data screening. As an exemplary scenario, hundreds of solutions that overcome the intrinsic signal saturation have been predicted by the inverse method, validating for a variety of material systems. The success in property design on multiple indicators demonstrates that the proposed inverse design is an efficient and powerful tool to target multifarious applications of flexible pressure sensors, which can potentially advance the fields of intelligent robots, advanced healthcare, and human-machine interfaces.
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Registered trials
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