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

DMREF/协作研究：加速未来纳米结构聚合物和复合材料系统设计的以数据为中心的方法

基本信息

批准号：
1729452
负责人：
Ravishankar Sundararaman
金额：
$ 79.06万
依托单位：
Rensselaer Polytechnic Institute
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1729452&HistoricalAwards=false
关键词：
DMREF Collaborative Research Data Centric

项目摘要

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.

聚合物纳米复合材料是高度可调节的材料，通过仔细设计，可以实现现有材料无法获得的优质特性。大多数聚合物纳米复合材料是使用爱迪生人（反复试验）过程开发的，严重限制了优化性能和增加实施时间的能力。解决方案是一种数据驱动的设计方法。例如，这种设计材料彻底改变和设计我们的未来（DMREF）项目将设计新的材料系统，以同时优化介电响应和机械耐用性，这是目前无法实现的组合，但对于高电压电气传输和转换而言是必需的。这些新材料将对社会产生重大的经济影响，因为它们将使发电效率更高和传播。更广泛地说，这种新的设计方法将导致新的纳米结构化聚合物材料系统，这些系统将影响能源，消费电子和制造等广泛的行业。为了确保对这项工作的广泛访问，开发的数据，工具和模型将通过开放数据资源纳米胺集成和共享。该团队将与科学界互动，以创建一个集成的虚拟组织设计师和研究人员，以测试和改进模型。 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新材料。数据，算法和模型将集成到新的纳米复合数据资源纳米胺中，以应对数据驱动的材料设计的挑战。这项研究将导致三个领域的进步。首先，将广泛的文献数据和靶向实验与多尺度方法相结合，将使相间模型的开发能够预测被认为对于对聚合物复合材料建模至关重要的接口附近的局部聚合物特性。其次，利用机器学习的混合方法将基于物理的建模域之间的桥梁长度尺度进行桥梁长度尺度，将用于创建有意义的多尺度处理结构 - 培训关系工作流。第三，贝叶斯推论方法将利用数据集中包含的知识作为先验的概率分布，并指导“按需”计算机模拟和物理实验，以加速对最佳材料设计的搜索。案例研究将证明以数据为中心的方法，以加速具有可预测和优化性能组合的下一代纳米结构聚合物的发展。