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: 2324168
• 负责人: Krzysztof Matyjaszewski
• 金额: $ 47.5万
• 依托单位: Carnegie-Mellon University
• 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=2324168&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：在我们的材料有限的世界中，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反复试验探索，以达到目标功能，而在回顾中发现结构与特殊关系仅效率低下。拟议的研究将通过为概念上的新过程建立基础来破坏这一现状，其中发现的分子代码将指示具有物理特性组合的材料的连续流综合。在软物质设计中实施数字代码将推动新的合成途径，刺激新的物理模型，并催化各种技术的基本转变，包括个性化医学，软机器人技术和可持续的材料制造。与阿克兰艺术博物馆的合作将教育学生在人类历史上不同文化中材料科学概念与艺术之间的联系。在这些知识的基础上，参与的学生将创建艺术品合并材料科学和艺术。此外，DMREF团队还将开发创建（考虑，阅读，阐明假设，分析和解释数据，并考虑下一个实验），以使本科生的创造性对研究设计和执行的创造力以及建立包容性环境。 DMREF团队还建议积极招募和支持妇女和人为不足的群体，包括但不限于残疾人和低收入学生。技术描述：该项目将通过整合分子设计代码，自动杂音和法医结构分析来追求可编程材料的开发。从临时响应的结构编码时间相关的属性和追溯解密的网络结构代表了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涵盖软材料组成，体系结构和临时演变的代码。这些定量描述符将用于人工智能指导的材料设计和聚合物网络的合成平台，其中新颖的在线表征技术和多个反馈循环实现了迭代性的多目标属性优化。逐构结构的设计方法将启动与分子结构中编码的特性组合的聚合物网络（弹性体，凝胶和热塑料）的可编程合成。根据材料基因组计划的任务，这项工作将创建一个具有多个嵌入式反馈循环的人工智能驱动的平台和一个开源大数据搜索引擎，可实现材料设计，合成和取证。该项目由NSF数学和物理科学（MPS）材料研究（DMR）和化学（CHE）通过设计材料进行革新和设计我们的未来（DMRREF）计划，DMR聚合物（POL）计划，以及MPS的多学科活动（MPS/OMA）。使用基金会的智力优点和更广泛的影响评估标准进行评估。