DMREF/Collaborative Research: A Data-Centric Approach for Accelerating the Design of Future Nanostructured Polymers and Composites Systems

聚合物纳米复合材料是高度可定制的材料，经过精心设计，可以获得现有材料无法获得的优越性能。大多数聚合物纳米复合材料都是采用爱迪生式（试错）工艺开发的，这严重限制了优化性能的能力，并增加了实施的时间。解决方案是一种数据驱动的设计方法。例如，设计材料以革新和工程我们的未来（DMREF）项目将设计同时优化介电响应和机械耐久性的新材料系统，这是目前无法实现的组合，但对于高压电力传输和转换是必要的。这些新材料将对社会产生重大的经济影响，因为它们将使发电和输电效率更高。更广泛地说，这种新的设计方法将导致新的纳米结构聚合物材料系统，这将影响广泛的行业，如能源、消费电子和制造业。为了确保这项工作的广泛获取，所开发的数据、工具和模型将通过一个开放的数据资源“纳米数据库”进行整合和共享。该团队将与科学界互动，创建一个由设计师和研究人员组成的综合虚拟组织，以测试和改进模型。教育部分将通过两所大学的跨学科集群项目覆盖本科生和研究生社区，并为本科生提供研究机会和基于网络的教学模块和研讨会。该研究基于一个中心研究假设，即使用基于物理的数据驱动方法，允许将从埃到毫米的长度尺度的模型集成在一起，以预测介电和机械性能，从而实现新材料的设计和优化。数据、算法和模型将集成到新的和不断增长的纳米复合材料数据资源NanoMine中，以解决数据驱动的材料设计中的挑战。这项研究将在三个领域取得进展。首先，通过多尺度方法整合广泛的文献数据和有针对性的实验，将能够开发界面模型，以预测对建模聚合物复合材料至关重要的界面附近的局部聚合物性质。其次，利用机器学习在基于物理的建模领域之间架起长度尺度的桥梁的混合方法将用于创建有意义的多尺度处理-结构-属性关系工作流。第三，贝叶斯推理方法将利用数据集中包含的知识作为先验概率分布，并指导“按需”计算机模拟和物理实验，以加速寻找最佳材料设计。案例研究将展示以数据为中心的方法，以加速具有可预测和优化组合特性的下一代纳米结构聚合物的开发。

Polymer nanocomposites are highly tailorable materials that, with careful design, can achieve superior properties not available with existing materials. Most polymer nanocomposites are developed using an Edisonian (trial and error) process, severely limiting the capacity to optimize performance and increasing time to implementation. The solution is a data-driven design approach. As an example, this Designing Materials to Revolutionize and Engineer our Future (DMREF) project will design new material systems that simultaneously optimize for dielectric response and mechanical durability, a combination currently not achievable but necessary for high voltage electrical transmission and conversion. These new materials will have a significant economic impact on society because they will enable higher efficiency generation and transmission of electricity. More broadly, this new design approach will result in new nanostructured polymer material systems that will impact a wide range of industries such as energy, consumer electronics, and manufacturing. To ensure broad access to this work, the data, tools and models developed will be integrated and shared through an open data resource, NanoMine. The team will interact with the scientific community to create an integrated virtual organization of designers and researchers to test and improve the models. Educational components will reach undergraduate and graduate communities via interdisciplinary cluster programs at the two institutions, and provide undergraduate research opportunities and web based instructional modules and workshops.The research is based on a central research hypothesis that using a data-driven approach, grounded in physics, allows integration of models that bridge length scales from angstroms to millimeters to predict dielectric and mechanical properties to enable the design and optimization of new materials. Data, algorithms and models will be integrated into the new and growing nanocomposite data resource NanoMine to address challenges in data-driven material design. This research will result in advancements in three areas. First, integrating a broad set of literature data and targeted experiments with multiscale methods will enable the development of interphase models to predict local polymer properties near interfaces considered critical for modeling polymer composites. Second, a hybrid approach utilizing machine-learning to bridge length scales between physics-based modeling domains will be used to create meaningful multiscale processing-structure-property relationship work flows. And, third, a Bayesian inference approach will utilize the knowledge contained in a dataset as a prior probability distribution and guide 'on-demand' computer simulations and physical experiments to accelerate the search of optimal material designs. Case studies will demonstrate the data-centric approach to accelerate the development of next-generation nanostructured polymers with predictable and optimized combinations of properties.