DMREF: Data-Driven Integration of Experiments and Multi-Scale Modeling for Accelerated Development of Aluminum Alloys

传统上，材料设计是一个迭代的过程，在这个过程中，通过调整材料的制造工艺来逐步提高材料的性能。通过在循环中引入计算模型并将输出与材料每一步的结构和性能的实验测量进行比较，可以加快这种方法的速度。然而，由于数据收集、分析和还原几乎每一步都需要人类参与，这一周期变得缓慢起来。设计材料革新和设计我们的未来(DMREF)奖支持研究，通过利用现代数据科学工具开发数据框架和基础设施，促进先进材料快速应用于工程应用，从而无缝集成复杂材料开发问题中的实验和模型。这一框架将被广泛应用，允许研究人员将其应用于特定问题，并允许将这些想法整合到广泛的材料和应用领域。所开发的概念和工具将通过免费提供的软件、开放源码模块、在线教程、开放获取数据集以及对用户在新的环境中应用这些工具的培训来传播。这项工作力求为材料设计循环建立一个新的范例，其中以数据流而不是个别的建模或实验任务为中心。现代数据科学工具将被用来创建在复杂材料开发问题中无缝集成实验和模型所需的语义框架和物理基础设施。该框架将定义和描述材料数据的类别以及这些类别之间的联系，这些类别是创建自动数据流所必需的，在该数据流中，设计循环中的每个实验和计算任务自动将信息推送到所有任务所共有的数据层，或从该数据层拉出信息。这将通过最大限度地减少所需的人工干预来加速材料设计过程，同时仍然允许在必要的地方将人工输入到回路中。这种方法的一个具体实例将是实施商业铝合金抗剥落破坏能力的多尺度建模框架，并通过一个集中的数据层自动连接到高级微观结构表征和高通量激光冲击试验。设计循环将通过进一步耦合到加工过程中的微结构发展模型来闭合，并通过经过训练的机器学习算法来优化微结构以抵抗层裂空洞的成核和生长。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Traditionally, materials design is an iterative process in which the properties of the material are gradually improved by adjusting process by which the material is made. This approach can be accelerated by introducing computational models into the loop and comparing the output with experimental measurements of the structure and properties of the material at each step. The cycle is slowed, however, by the need for humans to be involved at almost every step for data collection, analysis, and reduction. This Designing Materials to Revolutionize and Engineer our Future (DMREF) award supports research to facilitate rapid adoption of advanced materials into engineering applications by leveraging modern data science tools to develop a data framework and infrastructure for seamless integration of experiments and models in complex materials development problems. This framework will be broadly applicable, allowing researchers to adopt it to particular problems and allowing the ideas to be integrated across a wide range of materials and application areas. The concepts and tools developed will be disseminated through freely-available software, open source modules, online tutorials, open access data sets, and training for users to apply the tools in new contexts.This work seeks to establish a new paradigm for the materials design loop in which the flow of data, rather than individual modeling or experimental tasks, is viewed as central. Modern data science tools will be leveraged to create the semantic framework and physical infrastructure necessary for seamless integration of experiments and models in complex materials development problems. This framework will define and describe classes of material data, and connections among these classes, that are required to create an automated data flow in which each experimental and computational task in the design loop automatically pushes information to, or pulls information from, a data layer common to all tasks. This will accelerate the materials design process by minimizing the required human intervention, while still allowing human input to the loop where essential. A specific instantiation of this approach will be the implementation of a multi-scale modeling framework for the resistance of commercial aluminum alloys to spall failure, with automated connections to advanced microstructural characterization and high-throughput laser shock testing through a centralized data layer. The design loop will be closed by further coupling to models of microstructure development during processing, with machine-learning algorithms trained to optimize the microstructure to resist the nucleation and growth of spall voids.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.