CIF21 DIBBs: EI: Data Laboratory for Materials Engineering

CIF21 DIBB：EI：材料工程数据实验室

• 批准号: 1640867
• 负责人: Venugopal Govindaraju
• 金额: $ 290.98万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1640867&HistoricalAwards=false
• 关键词: CIF21; DIBBs; EI; Data; Laboratory

This project directly addresses the goals of the Materials Genome Initiative -- to accelerate the pace of discovery and deployment of advanced material systems. To obtain insights for the discovery of new materials and to study existing materials, scientists and engineers rely heavily on an ever-growing number of materials research databases and scholarly research publications that date back many decades. New materials innovation often takes years, sometimes decades, to develop a new material. The project addresses the challenges through several steps, including automatic extraction of data from relevant electronic publications, storage of the data in formats that support comparison and analysis, development of advanced computational tools to improve the analysis, integration of the tools into a data laboratory to support the discovery of new trends and relationships among materials properties, and to predict new materials with desired properties.The infrastructure building blocks developed under this project enable researchers to (i) use document processing technologies to process scientific publications and data from scientific databases in materials science to create a knowledge base; (ii) use machine learning technologies to learn from the data in this enhanced knowledge base to address a variety of use cases in materials science and engineering; and (iii) use innovative information retrieval and visualization tools for insightful analysis, facilitating faster discovery of new materials. The tools will be hosted and disseminated through a web portal built on the HubZero platform, which will also provide users with the ability to query and visualize data, and run simulations and experiments. The data laboratory portal also provides the ability to run simulations and experiments on high-performance computing clusters using the building blocks. The data laboratory provides a platform for materials informatics, enabling prediction of properties of metal alloys and interaction with the materials discovery and engineering user community. While the primary target is the interdisciplinary field of materials research, the tools are designed to be domain agnostic as the core technologies can be applied to documents and databases across a broad swath of disciplines to enhance the pace of scientific discoveries.This award by the Advanced Cyberinfrastructure Division is jointly supported by the NSF Directorate for Mathematical & Physical Sciences (Division of Materials Research).

该项目直接涉及材料基因组计划的目标-加快发现和部署先进材料系统的步伐。 为了获得发现新材料和研究现有材料的见解，科学家和工程师在很大程度上依赖于数量不断增长的材料研究数据库和学术研究出版物，这些数据库和出版物可以追溯到几十年前。 新材料的创新往往需要数年，甚至数十年的时间来开发一种新材料。 该项目通过几个步骤应对这些挑战，包括从相关电子出版物中自动提取数据，以支持比较和分析的格式存储数据，开发先进的计算工具以改进分析，将这些工具集成到数据实验室以支持发现新的趋势和材料特性之间的关系，该项目开发的基础设施模块使研究人员能够（i）使用文件处理技术处理材料科学科学科学数据库中的科学出版物和数据，以创建知识库;（ii）使用机器学习技术从这个增强的知识库中的数据中学习，以解决材料科学和工程中的各种用例;（iii）使用创新的信息检索和可视化工具进行深入分析，促进更快地发现新材料。 这些工具将通过建立在HubZero平台上的门户网站托管和传播，该平台还将使用户能够查询和可视化数据，并进行模拟和实验。数据实验室门户还提供了使用构建块在高性能计算集群上运行模拟和实验的能力。 数据实验室为材料信息学提供了一个平台，可以预测金属合金的性能，并与材料发现和工程用户社区进行互动。 虽然主要目标是材料研究的跨学科领域，但这些工具的设计与领域无关，因为核心技术可以应用于广泛学科的文档和数据库，以加快科学发现的步伐。该奖项由高级网络基础设施部门颁发，由NSF数学物理科学理事会（材料研究部门）共同支持。

项目成果

Content Extraction from Lecture Video via Speaker Action Classification Based on Pose Information

基于姿势信息的演讲者动作分类从讲座视频中提取内容

• DOI: 10.1109/icdar.2019.00171
• 发表时间: 2019
• 期刊: 2019 International Conference on Document Analysis and Recognition (ICDAR
• 作者: Xu, Fei;Davila, Kenny;Setlur, Srirangaraj;Govindaraju, Venu
• 通讯作者: Govindaraju, Venu

Exploring the shape of data for discovering patterns in crystal chemistry

探索数据的形状以发现晶体化学的模式

• DOI: 10.1557/s43579-021-00097-x
• 发表时间: 2021
• 期刊: MRS Communications
• 影响因子: 1.9
• 作者: Broderick, Scott;Dongol, Ruhil;Rajan, Krishna
• 通讯作者: Rajan, Krishna

A spatial analysis of the development potential of rooftop and community solar energy

屋顶及社区太阳能发展潜力空间分析

• DOI: 10.1016/j.rsase.2020.100355
• 发表时间: 2020
• 期刊: Remote Sensing Applications: Society and Environment
• 作者: Schunder, Torsten;Yin, Dameng;Bagchi-Sen, Sharmistha;Rajan, Krishna
• 通讯作者: Rajan, Krishna

Coupling of First Principles and Machine Learning for High Throughput Calculation of Evaporation Fields

第一原理与机器学习的耦合用于蒸发场的高通量计算

• DOI: 10.1017/s1431927618003112
• 发表时间: 2018
• 期刊: Microscopy and Microanalysis
• 影响因子: 2.8
• 作者: Peralta, Joaquin;Broderick, Scott;Loyola, Claudia;Rajan, Krishna
• 通讯作者: Rajan, Krishna

Discovering chemical site occupancy- modulus correlations in Ni based intermetallics via statistical learning methods

• DOI: 10.1016/j.cocom.2017.11.001
• 发表时间: 2018-03
• 期刊: Computational Condensed Matter
• 影响因子: 2.1
• 作者: Scott R. Brodericka;Aakash Kumarb;Adedapo A. Onid;James M. LeBeaud;S. B. Sinnottb;K. Rajana