Workshop on the Convergence of Materials Research and Multi-Sensory Data Science

材料研究中大型数据集的创建正在改变材料研究领域的工作方式，使数据科学成为发现新材料和阐明结构/加工/性能相关性的重要工具之一。目前，许多与技术相关的材料问题本质上是数据密集型的。缺乏将复杂的原始数据处理成可靠的科学知识的适当框架阻碍了关键领域的进展，例如材料微观结构的晶界特征分布及其许多自由度与热机械特性的相关性；以及控制正常和异常晶粒生长的机制的识别，以工程块状、纳米颗粒材料等。由于目前对如何处理和分析大数据的理解不足，缺乏可用的框架往往导致已获取的数据被丢弃。简而言之，材料科学家被来自许多来源的多维数据淹没，包括实验数据和计算数据，大量数据没有得到充分利用或被完全丢弃。本次研讨会的目的是通过组装四个看似不同的平台来构建所需的框架：人机交互，数据分析，材料基础研究和新工具。本次研讨会提出了一个真正的融合方法，融合了NSF的两个大想法，其中追求通过改进人机交互来利用数据，为材料发现提供了一个新的框架。本次研讨会的产出包括指导框架的路线图，以及参与者之间跨学科合作的核心和一些教育机会，包括招聘，早期社区建设和适合大学课程的公开教育材料。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 creation of large data sets in materials research is transforming the way in which work is done in the materials research field, making data science one of the essential tools in the discovery of new materials and in the elucidation of structure/processing/property correlations. At present, many materials problems of technological relevance are inherently data-intensive. Lack of adequate frameworks to process complex raw data into sound scientific knowledge hinders progress in crucial fronts, such as the correlation of grain-boundary character distribution of materials microstructures, along with its many degrees of freedom, with thermo-mechanical properties; and the identification of mechanisms controlling both normal and abnormal grain growth to engineer bulk, nanograin materials, amongst others. Lack of available frameworks often results in acquired data being discarded, given the poor current understanding on how to process and analyze big data. In short, materials scientists are inundated with multi-dimensional data from many sources, both experimental and computational, and vast amounts of data are under-utilized or thrown out completely. The purpose of this workshop is to build the needed framework by assembling four seemingly disparate platforms: Human Computer Interactions, Data Analytics, Fundamental Research in Materials, and New Tools. This workshop proposes a genuine convergence approach merging two NSF Big Ideas, where the pursuit of harnessing data through improved human-machine interaction presents a new framework for materials discovery. Outputs to this workshop include a roadmap to guide the framework, as well as the nucleation of transdisciplinary collaborations amongst participants and several education opportunities, including recruitment, early-community building, and publicly available education materials suitable for university courses. 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.