Intelligent electroactive material systems with self- adaptive mechanical memory and sequential logic

• 成果类型: Article
• 署名作者: El Helou, Charles; Hyatt, Lance P.; Buskohl, Philip R.; Harne, Ryan L.
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; United States Department of Defense; United States Air Force; US Air Force Research Laboratory
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9334
• DOI: 10.1073/pnas.2317340121
• 发表日期: 2024-04-02
• 关键词: neural-networks

摘要

By synthesizing the requisite functionalities of intelligence in an integrated material system, it may become possible to animate otherwise inanimate matter. A significant challenge in this vision is to continually sense, process, and memorize information in a decentralized way. Here, we introduce an approach that enables all such functionalities in a soft mechanical material system. By integrating nonvolatile memory with continuous processing, we develop a sequential logic - based material design framework. Soft, conductive networks interconnect with embedded electroactive actuators to enable self - adaptive behavior that facilitates autonomous toggling and counting. The design principles are scaled in processing complexity and memory capacity to develop a model 8 - bit mechanical material that can solve linear algebraic equations based on analog mechanical inputs. The resulting material system operates continually to monitor the current mechanical configuration and to autonomously search for solutions within a desired error. The methods created in this work are a foundation for future synthetic general intelligence that can empower materials to autonomously react to diverse stimuli in their environment.