NSF Convergence Accelerator Track D: A Community Resource for Innovation in Polymer Materials
NSF 融合加速器轨道 D:高分子材料创新的社区资源
基本信息
- 批准号:2040636
- 负责人:
- 金额:$ 100万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-09-15 至 2021-11-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This project, NSF Convergence Accelerator Track D: A Community Resource for Innovation in Polymer Materials, will develop a data-centric infrastructure for findable, accessible, interoperable, and reusable (FAIR) data that will be usable by a wide variety of stakeholders to accelerate the pace of materials innovation. The field is currently hampered by small, disparate data sets and there is clear need for a community-wide database effort. This project brings together a team with members from industry (Dow, Citrine), academia (MIT), and government laboratories (NIST) to directly address the issue of building a data and modeling infrastructure to serve as a community resource for polymeric material design. The project focuses on a sharing infrastructure for polymers and other soft materials addressing the current inability to deal with molecular distributions and stochastic reaction networks; characterization/data generation challenges for stochastic chemistry; challenges with nomenclature and molecular representation, and polymer properties determined on multiple scales from the chemical bond to the molecule to collective molecular interactions. The approach will utilize novel graph-based representations that can be widely adopted for the storage and exchange of data by all stakeholders in the polymer field. It will also explore how widely such data could be shared by different stakeholders, including paradigms that mix embargoed and open data as well as exploring models for ownership and credit that enable wider sharing of data across the community. The approach will employ natural language processing (NLP) and computer vision techniques for automated information extraction from the polymer literature to generate a large, public structure- property database. The text-based extraction schemes will exploit chemical rationales and specific shared synthesis techniques for more efficient data extraction; extraction of polymer chemical structures from images using an optical chemical structure recognition system; and development of new machine learning methods of data curation in order to integrate a wider range of data and overcome data sparsity and diversity. Together, these elements will yield a populated data structure by the end of Phase I that will form a foundation for further efforts by the community. Although the techniques employed will generalize to all synthetic polymers, the initial testbed for these developments will be polyurethanes—a large polymer market with diverse chemistry, substantial data availability in the patent and journal literature, and structure-processing-property relationships that are a playground for continued material innovation. Community-wide engagement will be sought via a digital symposium to assist in identifying additional interests and partners that need to be represented and included; disseminating information about this effort; and receiving inputs on how deliverables should be planned and designed during Phase II execution. An educational planning exercise in Phase I will identify educational partners and educational needs in this space and develop both pedagogical and assessment plans that can be acted upon in Phase II such that the tools that are built are available freely to the community via wide dissemination of knowledge and training. All of the tools and standards will be made publicly available using open-source development projects.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.
该项目,NSF融合加速器轨道D:聚合物材料创新的社区资源,将开发一个以数据为中心的基础设施,用于可查找,可访问,可互操作和可重用(FAIR)数据,这些数据将被各种利益相关者使用,以加快材料创新的步伐。该领域目前受到小而分散的数据集的阻碍,显然需要全社区的数据库工作。该项目汇集了一个团队,成员来自工业界(陶氏化学,Citrine),学术界(麻省理工学院)和政府实验室(NIST),直接解决建立数据和建模基础设施的问题,作为聚合物材料设计的社区资源。该项目的重点是聚合物和其他软材料的共享基础设施,解决目前无法处理分子分布和随机反应网络的问题;随机化学的表征/数据生成挑战;命名和分子表示的挑战,以及从化学键到分子到集体分子相互作用的多个尺度上确定的聚合物特性。该方法将利用新的基于图形的表示,可以广泛用于聚合物领域所有利益相关者的数据存储和交换。它还将探讨不同利益攸关方可以在多大程度上广泛共享这些数据,包括混合禁运和开放数据的范例,以及探索所有权和信贷模式,使整个社区能够更广泛地共享数据。该方法将采用自然语言处理(NLP)和计算机视觉技术,从聚合物文献中自动提取信息,以生成一个大型的公共结构-性能数据库。基于文本的提取方案将利用化学原理和特定的共享合成技术,以更有效地提取数据;使用光学化学结构识别系统从图像中提取聚合物化学结构;开发新的数据管理机器学习方法,以整合更广泛的数据并克服数据稀疏和多样性。这些元素将在第一阶段结束时产生一个填充的数据结构,这将为社区的进一步努力奠定基础。虽然所采用的技术将推广到所有合成聚合物,但这些发展的最初试验平台将是聚合物-一个具有不同化学性质的大型聚合物市场,专利和期刊文献中的大量数据以及结构-加工-性能关系是持续材料创新的游乐场。将通过数字研讨会寻求全社区的参与,以协助确定需要代表和包括的其他利益和合作伙伴;传播有关这一努力的信息;并接收关于在第二阶段执行期间应如何规划和设计可交付成果的投入。第一阶段的教育规划工作将确定这一领域的教育伙伴和教育需求,并制定可在第二阶段采取行动的教学和评估计划,以便通过广泛传播知识和培训,向社区免费提供所建立的工具。所有的工具和标准都将通过开源开发项目公开提供。该奖项反映了NSF的法定使命,并被认为值得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来支持。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Random Forest Predictor for Diblock Copolymer Phase Behavior
- DOI:10.1021/acsmacrolett.1c00521
- 发表时间:2021-10-14
- 期刊:
- 影响因子:7.015
- 作者:Arora, Akash;Lin, Tzyy-Shyang;Olsen, Bradley D.
- 通讯作者:Olsen, Bradley D.
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Bradley Olsen其他文献
EXPANSE: A time-of-flight EXPanded Angle Neutron Spin Echo spectrometer at the Second Target Station of the Spallation Neutron Source.
EXPANSE:散裂中子源第二目标站的飞行时间扩展角中子自旋回波光谱仪。
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:1.6
- 作者:
Changwoo Do;R. Ashkar;Cristina Boone;Wei;G. Ehlers;P. Falus;A. Faraone;J. Gardner;V. Graves;Thomas Huegle;Reika Katsumata;Darian Kent;Jiao Y. Y. Lin;Bill McHargue;Bradley Olsen;Yangyang Wang;Danielle Wilson;Y. Z - 通讯作者:
Y. Z
Bradley Olsen的其他文献
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{{ truncateString('Bradley Olsen', 18)}}的其他基金
NSF Convergence Accelerator Track D: A Community Resource for Innovation in Polymer Technology (CRIPT)
NSF 融合加速器轨道 D:聚合物技术创新社区资源 (CRIPT)
- 批准号:
2134795 - 财政年份:2021
- 资助金额:
$ 100万 - 项目类别:
Cooperative Agreement
RAPID: Collaborative Research: Augmenting Mucosal Gels with Associating Brush Polymers to Prevent COVID-19 Infection
RAPID:合作研究:用缔合刷状聚合物增强粘膜凝胶以预防 COVID-19 感染
- 批准号:
2029751 - 财政年份:2020
- 资助金额:
$ 100万 - 项目类别:
Standard Grant
Dynamics of Associative Polymers Revealed by Self-Diffusion
自扩散揭示缔合聚合物的动力学
- 批准号:
1709315 - 财政年份:2017
- 资助金额:
$ 100万 - 项目类别:
Standard Grant
Engineering a new family of consensus repeat proteins based on nucleoporins
基于核孔蛋白设计一个新的共有重复蛋白家族
- 批准号:
1705923 - 财政年份:2017
- 资助金额:
$ 100万 - 项目类别:
Standard Grant
CAREER: Self-Assembly of Fusion Proteins to Form Biofunctional Materials
职业:融合蛋白自组装形成生物功能材料
- 批准号:
1253306 - 财政年份:2013
- 资助金额:
$ 100万 - 项目类别:
Continuing Grant
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