Collaborative Research: Framework: Data: HDR: Nanocomposites to Metamaterials: A Knowledge Graph Framework

合作研究：框架：数据：HDR：纳米复合材料到超材料：知识图框架

• 批准号: 1835782
• 负责人: Wei Chen
• 金额: $ 59.98万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-11-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835782&HistoricalAwards=false
• 关键词: Collaborative; Research; Framework; Data; HDR

A team of experts from four universities (Duke, RPI, Caltech and Northwestern) creates an open source data resource for the polymer nanocomposites and metamaterials communities. A broad spectrum of users will be able to query the system, identify materials that may have certain characteristics, and automatically produce information about these materials. The new capability (MetaMine) is based on previous work by the research team in nanomaterials (NanoMine). The effort focuses upon two significant domain problems: discovery of factors controlling the dissipation peak in nanocomposites, and tailored mechanical response in metamaterials motivated by an application to personalize running shoes. The project will significantly improve the representation of data and the robustness with which user communities can identify promising materials applications. By expanding interaction of the nanocomposite and metamaterials communities with curated data resources, the project enables new collaborations in materials discovery and design. Strong connections with the National Institute of Standards and Technology (NIST), the Air Force Research Laboratory (AFRL), and Lockheed Martin facilitate industry and government use of the resulting knowledge base. The project develops an open source Materials Knowledge Graph (MKG) framework. The framework for materials includes extensible semantic infrastructure, customizable user templates, semi-automatic curation tools, ontology-enabled design tools and custom user dashboards. The work generalizes a prototype data resource (NanoMine) previously developed by the researchers, and demonstrates the extensibility of this framework to metamaterials. NanoMine enables annotation, organization and data storage on a wide variety of nanocomposite samples, including information on composition, processing, microstructure and properties. The extensibility will be demonstrated through creation of a MetaMine module for metamaterials, parallel to the NanoMine module for nanocomposites. The frameworks will allow for curation of data sets and end-user discovery of processing-structure-property relationships. The work supports the Materials Genome Initiative by creating an extensible data ecosystem to share and re-use materials data, enabling faster development of materials via robust testing of models and application of analysis tools. The capability will be compatible with the NIST Material Data Curator System, and the team also engages both AFRL and Lockheed Martin to facilitate industry and government use of the resulting knowledge base. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Materials Research within the NSF Directorate for Mathematical and Physical Sciences.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.

来自四所大学（杜克、RPI、加州理工学院和西北大学）的专家团队为聚合物纳米复合材料和超材料社区创建了一个开源数据资源。 广泛的用户将能够查询系统，识别可能具有某些特性的材料，并自动生成有关这些材料的信息。 新的能力（MetaMine）是基于纳米材料研究小组（NanoMine）以前的工作。 这项工作集中在两个重要的领域问题：发现控制纳米复合材料中耗散峰的因素，以及由个性化跑鞋应用程序激发的超材料中的定制机械响应。 该项目将显著改善数据的表现形式和用户群体识别有前途的材料应用的稳健性。 通过扩大纳米复合材料和超材料社区与策展数据资源的互动，该项目实现了材料发现和设计方面的新合作。 与美国国家标准与技术研究所（NIST）、空军研究实验室（AFRL）和洛克希德·马丁公司的紧密联系促进了行业和政府对所产生的知识库的使用。该项目开发了一个开源的材料知识图（MKG）框架。 材料框架包括可扩展的语义基础设施，可定制的用户模板，半自动策展工具，支持本体的设计工具和自定义用户仪表板。 这项工作概括了研究人员先前开发的原型数据资源（NanoMine），并展示了该框架对超材料的可扩展性。 NanoMine能够对各种纳米复合材料样品进行注释、组织和数据存储，包括成分、加工、微观结构和性能信息。 可扩展性将通过创建一个MetaMine模块的超材料，平行于纳米复合材料的NanoMine模块。 这些框架将允许管理数据集和最终用户发现过程-结构-属性关系。 这项工作支持材料基因组计划，通过创建一个可扩展的数据生态系统来共享和重用材料数据，通过强大的模型测试和分析工具的应用来加快材料的开发。 该能力将与NIST材料数据管理系统兼容，该团队还与AFRL和洛克希德·马丁公司合作，以促进行业和政府使用由此产生的知识库。该奖项由高级网络基础设施办公室颁发，由NSF数学和物理科学理事会材料研究部共同支持。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。

项目成果

Globally Approximate Gaussian Processes for Big Data With Application to Data-Driven Metamaterials Design

• DOI: 10.1115/1.4044257
• 发表时间: 2019-09
• 期刊: Journal of Mechanical Design
• 影响因子: 3.3
• 作者: R. Bostanabad;Yu-Chin Chan;Liwei Wang;P. Zhu;Wei Chen

METASET: Exploring Shape and Property Spaces for Data-Driven Metamaterials Design

• DOI: 10.1115/1.4048629
• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: Yu-Chin Chan;Faez Ahmed;Liwei Wang;Wei Chen

A perspective on the data-driven design of polymer nanodielectrics

• DOI: 10.1088/1361-6463/ab8b01
• 发表时间: 2020-04
• 期刊: Journal of Physics D: Applied Physics
• 作者: L. Schadler;L. Brinson;Wei Chen;R. Sundararaman;P. Gupta;Prajakta Prabhune;Akshay Iyer;Yixing Wang;Abhishek Shandilya

t-METASET: Task-Aware Generation of Metamaterial Datasets by Diversity-Based Active Learning

t-METASET：通过基于多样性的主动学习生成超材料数据集的任务感知型

• DOI: 10.1115/detc2022-87653
• 发表时间: 2022
• 期刊: American Society of Mechanical Engineers
• 作者: Lee, Doksoo;Chan, Yu-Chin;Chen, Wei;Wang, Liwei;Chen, Wei
• 通讯作者: Chen, Wei

Data-Driven Topology Optimization with Multiclass Microstructures using Latent Variable Gaussian Process

• DOI: 10.1115/1.4048628
• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: Liwei Wang;Siyu Tao;Ping Zhu;Wei Chen