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.

来自四个大学（Duke，RPI，Caltech和Northwestern）的专家团队为聚合物纳米复合材料和超材料社区创建了开源数据资源。 各种各样的用户将能够查询系统，识别可能具有某些特征的材料，并自动产生有关这些材料的信息。 新功能（Metamine）基于研究小组纳米材料（纳米胺）的先前工作。 这项工作的重点是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，以及通过应用个性化跑步鞋的应用而量身定制的超材料的机械响应。 该项目将显着改善数据的表示以及用户社区可以识别有希望的材料应用程序的鲁棒性。 通过扩展纳米复合材料和超材料社区与精选的数据资源的相互作用，该项目可以在材料发现和设计方面进行新的合作。 与国家标准技术研究所（NIST），空军研究实验室（AFRL）以及洛克希德·马丁（Lockheed Martin）的牢固联系，并促进了行业和政府对由此产生的知识库的使用。该项目开发开源材料知识图（MKG）框架。 材料框架包括可扩展的语义基础架构，可自定义的用户模板，半自动策展工具，启用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），并证明了该框架对超材料的可扩展性。 纳米胺可以在各种纳米复合样本上进行注释，组织和数据存储，包括有关组成，加工，微结构和属性的信息。 可扩展性将通过为超材料的纳米复合材料的纳米胺模块平行而创建超材料的metamine模块。 该框架将允许策划数据集和最终用户发现处理结构 - 统治关系。 这项工作通过创建可扩展的数据生态系统来共享和重复使用材料数据，从而支持材料基因组计划，从而通过模型的强大测试和分析工具的应用来更快地开发材料。 该功能将与NIST材料数据策展人系统兼容，该团队还与AFRL和Lockheed Martin互动，以促进行业和政府对所得知识库的使用。 NSF数学和物理科学局内的材料研究部共同支持了高级网络基础设施办公室的奖项。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来进行评估的。

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

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

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

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

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