BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, and Disseminate
BD 辐条:辐条:中西部:协作:集成材料设计 (IMaD):利用、创新和传播
基本信息
- 批准号:1636950
- 负责人:
- 金额:$ 72.25万
- 依托单位:
- 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:2017-02-15 至 2022-01-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Materials innovation is a pipeline, deriving from a deep understanding and control of material behavior and properties, leading to advanced materials designs that advance economic prosperity, address national and regional energy needs, and bolster national security. Improving this pipeline requires connecting independent but thematically congruent national and regional materials design efforts to align key stakeholders, consolidate diverse materials data expertise, simplify data access, coalesce on topics of data description and interoperability, enhance and ensure the quality of datasets, and deploy scalable data services to support materials researchers. The Midwest Big Data Spoke (MBD Spoke) for Integrative Materials Design (IMaD) connects researchers in industry, universities, and government to the people and services needed to easily find, access, and use data, tools, and services for materials design. The Midwest is the ideal place for such a program. Many major national materials design efforts funded by DOE, NIST, and NSF as part of the Materials Genome Initiative (MGI) operate in the Midwest, and the Midwest is home to major manufacturing industries that depend critically on materials innovation for their continued competitiveness.The technical work of IMaD will involve integration of software and services from across the Midwest and beyond, including the Materials Commons, the Materials Data Facility, NIST Materials Resource Registry, and Citrine Informatics, to enable smooth flow of software and data among these different systems. For example, integrated authentication provided by Globus Auth will enable access to different components with common credentials (e.g., institutional credentials), and integration of Globus transfer will allow for rapid and reliable exchange of large datasets. Common schemas and metadata terms will be developed and deployed to permit cross-system searching and display of information. Materials data from partners across the Midwest and beyond will be loaded into the Material Data Facility to permit easy discovery and access. Concurrently with these and other development activities, a series of workshops and meetings will be convened to engage academic, government, and industry participants in defining requirements for, and making use of, the integrated system for materials design.This award received co-funding from the Math and Physical Sciences Directorate (MPS) Division of Materials Research (DMR).
材料创新是一条管道,源于对材料行为和特性的深刻理解和控制,导致先进的材料设计,促进经济繁荣,满足国家和地区能源需求,并加强国家安全。改进这一管道需要连接独立但主题一致的国家和地区材料设计工作,以协调主要利益相关者,整合不同的材料数据专业知识,简化数据访问,合并数据描述和互操作性主题,增强和确保数据集的质量,并部署可扩展的数据集支持材料研究人员的数据服务。用于集成材料设计 (IMaD) 的中西部大数据辐条 (MBD 辐条) 将工业、大学和政府的研究人员与轻松查找、访问和使用材料设计的数据、工具和服务所需的人员和服务联系起来。中西部是实施此类计划的理想地点。作为材料基因组计划 (MGI) 的一部分,美国能源部、美国国家标准技术研究院和美国国家科学基金会资助的许多主要国家材料设计工作都在中西部开展,而中西部是主要制造业的所在地,这些制造业的持续竞争力严重依赖于材料创新。 IMaD 的技术工作将涉及中西部及其他地区的软件和服务的集成,包括 Materials Commons、Materials Data Facility、NIST Materials Resource Registration 和 Citrine Informatics,以实现软件和数据在这些机构之间的顺畅流动不同的系统。例如,Globus Auth 提供的集成身份验证将允许使用通用凭证(例如机构凭证)访问不同的组件,而 Globus 传输的集成将允许快速可靠地交换大型数据集。将开发和部署通用模式和元数据术语,以允许跨系统搜索和信息显示。来自中西部及其他地区合作伙伴的材料数据将被加载到材料数据设施中,以便轻松发现和访问。在这些和其他开发活动的同时,还将召开一系列研讨会和会议,让学术界、政府和行业参与者参与定义材料设计集成系统的要求并利用该系统。该奖项获得了以下机构的共同资助:数学和物理科学局 (MPS) 材料研究部 (DMR)。
项目成果
期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations
- DOI:10.1557/mrc.2019.107
- 发表时间:2019-06
- 期刊:
- 影响因子:1.9
- 作者:Logan T. Ward;B. Blaiszik;Ian T. Foster;R. Assary;B. Narayanan;L. Curtiss
- 通讯作者:Logan T. Ward;B. Blaiszik;Ian T. Foster;R. Assary;B. Narayanan;L. Curtiss
A data ecosystem to support machine learning in materials science
- DOI:10.1557/mrc.2019.118
- 发表时间:2019-12-01
- 期刊:
- 影响因子:1.9
- 作者:Blaiszik, Ben;Ward, Logan;Foster, Ian
- 通讯作者:Foster, Ian
Virtual Excited State Reference for the Discovery of Electronic Materials Database: An Open-Access Resource for Ground and Excited State Properties of Organic Molecules
- DOI:10.1021/acs.jpclett.9b02577
- 发表时间:2019-11-07
- 期刊:
- 影响因子:5.7
- 作者:Abreha, Biruk G.;Agarwal, Snigdha;Lopez, Steven A.
- 通讯作者:Lopez, Steven A.
Crowd-Sourced Data and Analysis Tools for Advancing the Chemical Vapor Deposition of Graphene: Implications for Manufacturing
促进石墨烯化学气相沉积的众包数据和分析工具:对制造业的影响
- DOI:10.1021/acsanm.0c02018
- 发表时间:2020
- 期刊:
- 影响因子:5.9
- 作者:Schiller, Joshua A.;Toro, Ricardo;Shah, Aagam;Surana, Mitisha;Zhang, Kaihao;Robertson, Matthew;Miller, Kristina;Cruse, Kevin;Liu, Kevin;Seong, Bomsaerah
- 通讯作者:Seong, Bomsaerah
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Ian Foster其他文献
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
DeepSpeed4Science 计划:通过复杂的人工智能系统技术实现大规模科学发现
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
S. Song;Bonnie Kruft;Minjia Zhang;Conglong Li;Shiyang Chen;Chengming Zhang;Masahiro Tanaka;Xiaoxia Wu;Jeff Rasley;A. A. Awan;Connor Holmes;Martin Cai;Adam Ghanem;Zhongzhu Zhou;Yuxiong He;Christopher Bishop;Max Welling;Tie;Christian Bodnar;Johannes Brandsetter;W. Bruinsma;Chan Cao;Yuan Chen;Peggy Dai;P. Garvan;Liang He;E. Heider;Pipi Hu;Peiran Jin;Fusong Ju;Yatao Li;Chang Liu;Renqian Luo;Qilong Meng;Frank Noé;Tao Qin;Janwei Zhu;Bin Shao;Yu Shi;Wen;Gregor Simm;Megan Stanley;Lixin Sun;Yue Wang;Tong Wang;Zun Wang;Lijun Wu;Yingce Xia;Leo Xia;Shufang Xie;Shuxin Zheng;Jianwei Zhu;Pete Luferenko;Divya Kumar;Jonathan Weyn;Ruixiong Zhang;Sylwester Klocek;V. Vragov;Mohammed Alquraishi;Gustaf Ahdritz;C. Floristean;Cristina Negri;R. Kotamarthi;V. Vishwanath;Arvind Ramanathan;Sam Foreman;Kyle Hippe;T. Arcomano;R. Maulik;Max Zvyagin;Alexander Brace;Bin Zhang;Cindy Orozco Bohorquez;Austin R. Clyde;B. Kale;Danilo Perez;Heng Ma;Carla M. Mann;Michael Irvin;J. G. Pauloski;Logan Ward;Valerie Hayot;M. Emani;Zhen Xie;Diangen Lin;Maulik Shukla;Thomas Gibbs;Ian Foster;James J. Davis;M. Papka;Thomas Brettin;Prasanna Balaprakash;Gina Tourassi;John P. Gounley;Heidi Hanson;T. Potok;Massimiliano Lupo Pasini;Kate Evans;Dan Lu;D. Lunga;Junqi Yin;Sajal Dash;Feiyi Wang;M. Shankar;Isaac Lyngaas;Xiao Wang;Guojing Cong;Peifeng Zhang;Ming Fan;Siyan Liu;A. Hoisie;Shinjae Yoo;Yihui Ren;William Tang;K. Felker;Alexey Svyatkovskiy;Hang Liu;Ashwin Aji;Angela Dalton;Michael Schulte;Karl Schulz;Yuntian Deng;Weili Nie;Josh Romero;Christian Dallago;Arash Vahdat;Chaowei Xiao;Anima Anandkumar;R. Stevens - 通讯作者:
R. Stevens
GreenFaaS: Maximizing Energy Efficiency of HPC Workloads with FaaS
GreenFaaS:利用 FaaS 最大限度提高 HPC 工作负载的能源效率
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Alok V. Kamatar;Valerie Hayot;Y. Babuji;André Bauer;Gourav Rattihalli;Ninad Hogade;D. Milojicic;Kyle Chard;Ian Foster - 通讯作者:
Ian Foster
An optical microscopy system for 3 D dynamic imagingRandy
用于 3D 动态成像的光学显微镜系统Randy
- DOI:
- 发表时间:
2007 - 期刊:
- 影响因子:0
- 作者:
R. Hudson;John N. Aarsvold;Chin;Jie Chen;Peter Davies;T. Disz;Ian Foster;Melvin Griem;Man K Kwong;B. Lin - 通讯作者:
B. Lin
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning
通过因果图分区进行高维结构化假设空间的因果发现
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Ashka Shah;Adela DePavia;Nathaniel Hudson;Ian Foster;Rick Stevens - 通讯作者:
Rick Stevens
Improving Seasonal Forecasts for SWWA
改进 SWWA 的季节性预测
- DOI:
- 发表时间:
2011 - 期刊:
- 影响因子:0
- 作者:
P. McIntosh;S. Asseng;O. Alves;E. Ebert;I. Farré;Ian Foster;N. Khimashia;M. Pook;J. Risbey;Dean Thomas;G. Thomas;Guomin Wang - 通讯作者:
Guomin Wang
Ian Foster的其他文献
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{{ truncateString('Ian Foster', 18)}}的其他基金
Collaborative Research: NSF Workshop on Automated, Programmable and Self Driving Labs
合作研究:NSF 自动化、可编程和自动驾驶实验室研讨会
- 批准号:
2335910 - 财政年份:2023
- 资助金额:
$ 72.25万 - 项目类别:
Standard Grant
Frameworks: Garden: A FAIR Framework for Publishing and Applying AI Models for Translational Research in Science, Engineering, Education, and Industry
框架:Garden:用于发布和应用人工智能模型进行科学、工程、教育和工业转化研究的公平框架
- 批准号:
2209892 - 财政年份:2022
- 资助金额:
$ 72.25万 - 项目类别:
Standard Grant
Collaborative Research: OAC Core: ScaDL: New Approaches to Scaling Deep Learning for Science Applications on Supercomputers
协作研究:OAC 核心:ScaDL:在超级计算机上扩展深度学习科学应用的新方法
- 批准号:
2107511 - 财政年份:2021
- 资助金额:
$ 72.25万 - 项目类别:
Standard Grant
NSF Convergence Accelerator Track D: The Data Hypervisor: Orchestrating Data and Models
NSF 融合加速器轨道 D:数据管理程序:编排数据和模型
- 批准号:
2040718 - 财政年份:2020
- 资助金额:
$ 72.25万 - 项目类别:
Standard Grant
Collaborative Research: Frameworks: funcX: A Function Execution Service for Portability and Performance
协作研究:框架:funcX:可移植性和性能的函数执行服务
- 批准号:
2004894 - 财政年份:2020
- 资助金额:
$ 72.25万 - 项目类别:
Standard Grant
Virtual Data Set Services Enabling New Science at NSF Facilities
虚拟数据集服务在 NSF 设施中实现新科学
- 批准号:
1841531 - 财政年份:2018
- 资助金额:
$ 72.25万 - 项目类别:
Standard Grant
Framework: Software: HDR Globus Automate: A Distributed Research Automation Platform
框架:软件:HDR Globus Automate:分布式研究自动化平台
- 批准号:
1835890 - 财政年份:2018
- 资助金额:
$ 72.25万 - 项目类别:
Standard Grant
EAGER: Designing the OSN Software Platform
EAGER:设计 OSN 软件平台
- 批准号:
1836357 - 财政年份:2018
- 资助金额:
$ 72.25万 - 项目类别:
Standard Grant
Collaborative Research: CyberSEES:Type 2: Framework to Advance Climate, Economics, and Impact Investigations with Information Technology (FACE-IT)
合作研究:CyberSEES:类型 2:利用信息技术推进气候、经济和影响调查的框架 (FACE-IT)
- 批准号:
1331922 - 财政年份:2013
- 资助金额:
$ 72.25万 - 项目类别:
Standard Grant
Collaborative Research: Managing Cloud Usage Allocation and Accounting for the NSF Community
协作研究:管理 NSF 社区的云使用分配和核算
- 批准号:
1250555 - 财政年份:2012
- 资助金额:
$ 72.25万 - 项目类别:
Standard Grant
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