IUCRC Planning Grant Carnegie Mellon University: Center for Materials Data Science for Reliability and Degradation (MDS-Rely)

IUCRC 规划拨款 卡内基梅隆大学：可靠性和退化材料数据科学中心 (MDS-Rely)

• 批准号: 2310663
• 负责人: John Kitchin
• 金额: $ 2万
• 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: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310663&HistoricalAwards=false
• 关键词: IUCRC; Planning; Grant; Carnegie; Mellon

This project proposes to plan a new site in the Center on Data Science for Materials Reliability and Degradation (MDS-Rely) by garnering industry support and feedback on applying data science-informed research to understand reliability and lifetime of materials, and use this feedback to ensure the Center is responsive to industry needs. Reliability of materials, parts and products are essential for US infrastructure in energy, defense, and national health and welfare. MDS-Rely will recruit potential industry partners interested in conducting research with the Center to apply data science to materials development. The planning grant will then garner feedback from these industry partners on the following objectives of the Center: (1) Enhance reliability testing, (2) Develop degradation models, (3) Apply degradation models, and (4) Develop educational programs. Together these efforts will enhance national interests.MDS-Rely will harness relationships between industry and academia to make headways in applying data science to applied materials development, design, and reliability. There will be three intellectual thrusts: (1) Soft and biomaterial formulation; (2) Natural language processing for data science of materials; and (3) Data-driven design of catalytic materials and processes. The soft and biomaterial formulation thrust will encompass machine-learning augmented design of experiments and automated science. The natural language processing thrust will focus on leveraging the existing literature more effectively in the area of materials reliability and degradation. Finally, the data-driven design of catalyst materials and processes will combine automated simulation with process systems engineering to discover materials and processes that are robust to degradation. The new site will leverage strengths at the intersections of computing, data science and machine learning with domain expertise in soft and biomaterials as well as catalysis at Carnegie Mellon University.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.

该项目建议通过获得行业支持和应用数据科学知识研究以了解材料的可靠性和寿命，并使用此反馈来确保该中心能够响应行业需求，从而计划在数据科学中心（MDS范围）的数据科学中心（MDS范围）计划新站点，以实现材料可靠性和退化（MDS范围）。材料，零件和产品的可靠性对于美国基础设施在能源，国防以及国家健康与福利方面至关重要。 MDS-ryly将招募有兴趣与中心进行研究的潜在行业合作伙伴，将数据科学应用于材料开发。然后，计划赠款将获得这些行业合作伙伴的反馈，内容涉及中心的以下目标：（1）增强可靠性测试，（2）开发退化模型，（3）采用退化模型，以及（4）开发教育计划。这些努力将共同提高国家利益。MDS瑞利将利用行业和学术界之间的关系，以将数据科学应用于应用材料开发，设计和可靠性。将有三个智力推力：（1）软体和生物材料的表述； （2）材料数据科学的自然语言处理； （3）催化材料和过程的数据驱动设计。柔软的生物材料配方推力将涵盖实验和自动科学的机器学习增强设计。自然语言处理的推力将集中在材料可靠性和退化领域中更有效地利用现有文献。最后，催化剂材料和流程的数据驱动设计将将自动模拟与过程系统工程相结合，以发现可降解的材料和过程。新站点将利用计算，数据科学和机器学习与软件和生物材料领域专业知识以及卡内基·梅隆大学（Carnegie Mellon University）的域名专业知识的相互作用的优势。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子的知识和更广泛的影响来通过评估的支持，并被认为是值得的。