HDR IDEAS^2 Institute: Data-Driven Frameworks for Materials Discovery

具有独特性能和功能的新材料的发现和开发彻底改变了整个行业，包括航空，航天，通信，生物医学和汽车。材料设计传统上是实验和计算密集型的。然而，数据驱动方法、计算能力和实验能力的进步为有针对性的高效材料设计创造了一个临界点。 利用数据革命研究所进行科学与工程数据密集型研究（HDR-I-DIRSE）框架奖支持研究所的概念化，以推进材料科学与工程的数据密集型研究。IDEAS ^2（加速随机科学的综合数据环境）材料发现研究所将为材料进步的实验和计算框架的开发提供一个平台，鼓励研究社区之间的合作和数据驱动方法的共享。数据科学方法本质上是可互操作的，该计划将使不同的研究社区参与协作开发适用于各种学科的大型数据框架。IDEAS^2研究所将通过各种机制降低领域科学家与数据科学家合作的障碍，包括一年两次的“教老师”研讨会、一年一次的IDEAS^2研讨会、UCSB的客座教师职位以及一系列其他社区参与活动。该项目的学生将获得宝贵的多学科研究和教育机会。热力学和动力学性质的第一性原理计算以及基于微观结构的高通量模型的信息将被整合到数据结构的设计和开发技术的分析中。开发的框架将基于机器学习方法，这些方法是基于基础的，计算和统计上易于处理的，并结合了领域知识和模拟结果。该研究所开发的框架和数据-例如从第一原理预测加工进步，以高通量方式对这些进步进行建模，实现高通量实验，调整所得实验数据（化学，微观结构，变形等），并有效地挖掘由此产生的高维数据集-将与开源平台（BisQue）集成，以促进内部和外部合作，开发广泛的材料应用。通过BisQue平台的计算基础设施和计算并行化能够筛选非常大的数据集，分层工作流程需要最低的软件要求（仅需要Web浏览器）和最少的用户在材料建模方面的领域知识。该计划的重点是对众多科学和技术领域具有重大和广泛影响的研究领域，它也代表了一个独特的培训机会，获得的技能将推动其毕业生走在新兴的，关键的数据驱动科学领域的最前沿，以及各种科学学科和高科技产业部门中的许多应用领域。该项目是美国国家科学基金会利用数据革命（HDR）大创意活动的一部分，由土木、机械和制造创新部门共同资助。该奖项反映了NSF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 discovery and development of new materials with unique properties and functionalities has revolutionized entire industries, including aviation, space, communication, biomedical, and automotive. Materials design has been traditionally experimentally and computationally intensive. However, advances in data-driven approaches, computational power, and experimental capabilities have created a tipping point for targeted and efficient materials design. This Harnessing the Data Revolution Institutes for Data-Intensive Research in Science and Engineering (HDR-I-DIRSE) Frameworks award supports conceptualization of an Institute to advance data-intensive research in Materials Science and Engineering. The IDEAS^2 (Integrated Data Environment for Accelerated Stochastic Science) Institute for Materials Discovery will provide a platform for the development of experimental and computational frameworks for materials advancement, that encourages collaboration and the sharing of data-driven approaches among research communities. The Data Science methods are intrinsically interoperable, and this program will engage diverse research communities in the collaborative development of large data frameworks that are applicable across a wide range of disciplines. The IDEAS^2 Institute will be structured to lower the barrier for domain scientists to work with data scientists through a variety of mechanisms including biannual "Teach the Teacher" workshops, an annual IDEAS^2 Symposium, visiting faculty positions at UCSB, and a range of other community engagement activities. Students working on this program will gain valuable multidisciplinary research and educational opportunities.First-principle calculations of thermodynamic and kinetic properties and information from microstructurally-based, high throughput models will be integrated into the design of data structures and the analyses of the developed techniques. The developed frameworks will be grounded in machine learning approaches that are fundamentally-based, computationally and statistically tractable, and incorporate domain knowledge and simulation results. The frameworks and data developed in the Institute - such as those to predict processing advancements from first principles, model these advancements in a high-throughput fashion, enable high-throughput experimentation, align the resulting experimental data (chemical, microstructure, deformation, etc.), and efficiently mine the resultant high-dimensional datasets - will be integrated with an open-source platform (BisQue) to facilitate both internal and external collaboration on their development for a broad range of materials applications. The computational infrastructure and parallelization of calculations through the BisQue platform enables the screening of very large datasets, with a hierarchical workflow requiring minimal software requirements (only a web browser is needed) and minimal domain knowledge of the user in modeling of materials. The focus of this program is on a research area with major and broad implications on numerous scientific and technological fields, and it also represents a unique training opportunity with acquired skills that will propel its graduates to the forefront of the emerging, critical field of data-driven science, as well as its many application areas within various scientific disciplines and high-tech industry sectors. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity and is co-funded by the Division of Civil, Mechanical and Manufacturing Innovation.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.