IUCRC Phase I: Case Western Reserve University (CWRU): Center for Materials Data Science for Reliability and Degradation (MDS-Rely)

可靠性与退化材料数据科学中心（MDS-Rely）是凯斯西储大学（CWRU）和匹兹堡大学（皮特）联合建立的产学研合作研究中心（IUCRC）。数据科学方法可以彻底改变美国能源、国防和交通基础设施的材料、零部件和产品的开发、制造和生命周期应用。MDS-Rely将通过整合材料价值链的研究和创新来应对技术和社会挑战，从而提高学生、员工和更广泛劳动力的能力。MDS-Rely的目标包括开发材料可靠性、降解和寿命性能问题的解决方案，基于健壮的开源代码和数据集，再加上改进的研究方案和标准，可以有效地实施。该团队还寻求创建一个跨领域的社区，以解决材料、组件和系统的降解和失效所带来的社会和材料挑战，并通过开发材料数据科学解决方案，联合行业、国家实验室和学术研究人员来应对这些挑战。最后，该团队寻求为多样化的STEM劳动力开发教育计划，为材料和数据科学领域的动态职业做好准备，并承担具有工业和商业影响的研究问题。通过独特的团队研究能力和基础设施资源，MDS-Rely将开发可靠性、性能和退化解决方案，建立标准和可靠性研究协议，并开发材料数据科学代码、封装和数据集，为聚合物、弹性体、涂料、金属、合金、半导体和光电子等材料价值链提供信息。这项研究将在三个重点领域实现：风化和性能，减法和增材制造，以及组件，设备和系统。项目包括但不限于：a)材料和系统中机械降解途径的网络模型；B)对图像进行机器学习，以识别缺陷和随曝光时间的退化过程；c)从层析图像中识别影响增材或传统制造零件可靠性的缺陷。这些新的材料数据科学研究协议和分析将使MDS-Rely成员能够在他们自己的研究和制造过程中实施最先进的方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The Center for Materials Data Science for Reliability and Degradation (MDS-Rely) is a joint industry/university cooperative research center (IUCRC) between Case Western Reserve University (CWRU) and University of Pittsburgh (Pitt). Data science-informed approaches can revolutionize the development, manufacturing, and lifecycle applications of materials, parts, and products for U.S. infrastructures in energy, defense, and transportation. MDS-Rely will improve the capabilities of students, employees, and the broader workforce by bringing together research and innovation in materials-based value chains to take on technological and societal challenges. The goals of MDS-Rely include developing solutions to materials reliability, degradation, and lifetime performance problems, based on robust open source codes and datasets, coupled with improved study protocols and standards that can be effectively implemented. The team also seeks to create a cross-cutting community to address the societal and materials challenges arising from degradation and failure of materials, components, and systems and to unite industry, national labs, and academic researchers to address these challenges by developing materials data science solutions. Finally, the team seeks to develop educational programs for a diverse STEM workforce prepared for dynamic careers in the materials and data sciences and to take on research problems with industrial and commercial impact. Through unique team research capabilities and infrastructure resources, MDS-Rely will develop reliability, performance, and degradation solutions, establish standards and reliability study protocols, and develop materials data science codes, packages and datasets to inform the materials value chains of polymers, elastomers, coatings, metals, alloys, semiconductors, and optoelectronics. This research will be achieved in three thrust areas: Weathering and Performance, Subtractive and Additive Manufacturing, and Components, Devices, and Systems. Projects include, but are not limited to: a) network models of mechanistic degradation pathways in materials and systems; b) machine learning on images to identify defects and the progress of degradation with exposure time; and c) identification of defects from tomographic images impacting the reliability of additively or traditionally manufactured parts. These new materials data science study protocols and analytics will enable MDS-Rely members to implement state of the art methods in their own research and manufacturing processes.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.