Collaborative Research: DMREF:Programmable Design, Synthesis, and Forensics of Soft Materials

合作研究：DMREF：软材料的可编程设计、合成和取证

• 批准号: 2324167
• 负责人: Sergei Sheiko
• 金额: $ 142.5万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324167&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Programmable; Design

Non-technical Description: In our materials-limited world, developing new materials is the foundation of technological progress. The current approach to materials design relies on trial-and-error exploration of chemicals and reactions to achieve a target function where structure–property relationships are identified only in retrospect is woefully inefficient. The proposed research will disrupt this status quo by developing a foundation for conceptually new processes, where discovered molecular codes will instruct the continuous flow synthesis of materials with encoded combinations of physical properties. Implementing digital codes in soft matter design will drive new synthetic pathways, stimulate new physical models, and catalyze fundamental shifts in various technologies, including personalized medicine, soft robotics, and sustainable materials fabrication. Collaboration with the Ackland Museum of Art will educate students on the connection between Materials Science concepts and Art in different cultures across human history. Building on this knowledge, participating students will create artwork merging Materials Science and Art. In addition, the DMREF team will develop the CREATE (Consider, Read, Elucidate the hypothesis, Analyze and interpret data, and Think of the next Experiment) method to give undergraduate students creative ownership of the research design and execution, as well as build an inclusive environment. This DMREF team also proposes to actively recruit and support women and underrepresented groups, including but not limited to people with disabilities and low-income students.Technical Description: The project will pursue the development of programmable materials by integrating molecular design codes, automated synthesis, and forensic structural analysis. Structurally encoding time-dependent properties and retroactively deciphering network structure from the temporal response represents one of the intellectual challenges of the project. State-of-the-art network theory and computer simulations will define a set of codes that encompass the composition, architecture, and temporal evolution of soft materials. These quantitative descriptors will be implemented into Artificial Intelligence-guided materials design and synthesis platforms for polymer networks, wherein novel in-line characterization techniques and multiple feedback loops enable iterative multi-objective property optimization. The design-by-architecture approach will launch the programmable synthesis of polymer networks (elastomers, gels, and thermoplastics) with tailored combinations of properties encoded in molecular structure. In line with the Materials Genome Initiative mission, this effort will create an Artificial Intelligence-driven platform with multiple embedded feedback loops and an open-source Big Data search engine that enable materials design, synthesis, and forensics. This project is co-funded by the NSF Mathematical and Physical Sciences (MPS) Divisions of Materials Research (DMR) and Chemistry (CHE) through the Designing Materials to Revolutionize and Engineer our Future (DMREF) program, the DMR Polymers (POL) Program, and the MPS Office of Multidisciplinary Activities (MPS/OMA).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非技术描述：在我们材料有限的世界中，开发新材料是技术进步的基础，当前的材料设计方法依赖于对化学物质和反应的试错探索，以实现结构-性能关系的目标功能。令人遗憾的是，拟议的研究将通过开发概念性新工艺的基础来打破这种现状，其中发现的分子代码将指导具有物理特性编码组合的材料的连续流动合成。物质设计将推动新的合成途径，刺激新的物理模型，并促进各种技术的根本性转变，包括个性化医疗、软机器人和可持续材料制造，与阿克兰艺术博物馆的合作将教育学生材料科学概念与艺术之间的联系。基于这些知识，参与的学生将创作融合材料科学和艺术的艺术品。此外，DMREF 团队将开发 CREATE（考虑、阅读、阐明假设、分析和）。解释数据和思考下一个实验）方法，为本科生提供研究设计和执行的创造性所有权，并建立一个包容性的环境。该 DMREF 团队还建议积极招募和支持女性和代表性不足的群体，包括但不包括女性和代表性不足的群体。仅限于残疾人和低收入学生。技术描述：该项目将通过集成分子设计代码、自动合成和法医结构分析来开发可编程材料，对时间依赖性特性进行结构编码，并追溯性地破译网络结构。时间响应代表了该项目的智力挑战之一，最先进的网络理论和计算机模拟将定义一组包含软材料的组成、结构和时间演化的代码。实施到人工智能引导的聚合物网络材料设计和合成平台中，然而，新颖的在线表征技术和多个反馈循环可实现迭代多目标性能优化，架构设计方法将启动聚合物网络的可编程合成。 （弹性体、凝胶和热塑性塑料）与分子结构中编码的特性的定制组合符合材料基因组计划的使命，这项工作将创建一个人工智能驱动的平台，具有多个嵌入式反馈循环和开源大数据搜索。该项目由 NSF 数学和物理科学 (MPS) 材料研究 (DMR) 和化学 (CHE) 部门通过 Designing 共同资助。革命和设计我们未来的材料 (DMREF) 计划、DMR 聚合物 (POL) 计划和 MPS 多学科活动办公室 (MPS/OMA)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。