CIF21 DIBBS: EI: The Local Spectroscopy Data Infrastructure (LSDI)

CIF21 DIBBS：EI：本地光谱数据基础设施 (LSDI)

• 批准号: 1640899
• 负责人: Kristin Persson
• 金额: $ 394.04万
• 依托单位: University of California-Berkeley
• 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: 2016-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1640899&HistoricalAwards=false
• 关键词: CIF21; DIBBS; EI; Local; Spectroscopy

Traditional empirical and 'one-at-a-time' materials testing is unlikely to meet the innovation needs in chemical and materials research, to enable emerging industries to address challenges in energy, national security, healthcare, and other areas in a timely manner. Historically, novel materials exploration has been slow and expensive, taking on average 18 years from concept to commercialization. This project has identified a major scientific challenge - characterization of materials and chemical systems via spectroscopy - that can greatly enhance and expand materials research through accumulation, organization, and automation of both experimental and computational resources and data. Currently, a large amount of time is invested in the interpretation and understanding of spectroscopic data, since no resource for efficiently accomplishing these tasks is available. This project allows materials researchers and chemists working in the spectroscopic field to access a searchable database of existing parameters and spectra for comparative, automated identification, and to address the full range of data elements -- production, curation, analysis, dissemination and sharing. The resulting data resource contributes to the cyberinfrastructure of the broader materials, chemistry, and engineering community, and has the potential to catalyze the discovery of new materials and the innovative use of materials and chemical systems in science and industry. The goal of the Local Spectroscopy Data Infrastructure (LSDI) project is to establish the first computational local atomic environment spectroscopy database, based on well-benchmarked computational spectra, to enable a publicly available, online resource for rapid material characterization, to accelerate materials development and optimization. Through novel technological advancements involving nanoscale engineering of defects, interfaces and surfaces, it has become increasingly important to determine the local atomic environments in materials. Spectroscopic techniques - including X-Ray Absorption Near Edge Spectroscopy (XANES), Extended X-Ray Absorption Fine Structure (EXAFS), Electron Energy Loss Spectroscopy (EELS), and Nuclear Magnetic Resonance (NMR) - have become essential characterization tools in elucidating atomic-scale chemical structure, electronic properties, and quantum phenomena in materials. There is a growing need for a general-use resource to help make spectral assignments for all researchers, including non- specialists, by capitalizing on recent advances in computational methods to populate an interactive database consisting of solid-state X-ray absorption and NMR spectra and associated parameters. This project includes: (i) creation of robust, benchmarked workflows for first-principles calculation of XAS/NMR spectra; (ii) data generation, curation and storage; (iii) development of automated spectral analysis algorithms; (iv) dissemination through the Materials Project; and (v) dynamic interaction with the community through the new Materials Data Cloud (MDCloud) environment. The data infrastructure developed by this project will allow a researcher who has recorded an experimental spectrum, such as by NMR, XANES or XAFS, of a solid-state material - even one with a disordered or non-crystalline structure - to access through the internet a searchable database of existing parameters and spectra for comparative, automated identification, along with a computational resource for simulating the spectra associated with various structural and chemical hypotheses. The LSDI contributes to the cyberinfrastructure of the materials, chemistry, and engineering communities, and supports advances in the fundamental understanding of spectroscopic methods, materials and chemical systems. The system catalyzes the discovery of new materials, and supports innovative use of materials and chemical systems in science and industry, consistent with the goals of the Materials Genome Initiative.This award by the Advanced Cyberinfrastructure Division is jointly supported by the NSF Engineering Directorate (Division of Civil, Mechanical & Manufacturing Innovation) and the NSF Directorate for Mathematical & Physical Sciences (Division of Chemistry and Division of Materials Research).

传统的经验和“一次性”材料测试不太可能满足化学和材料研究的创新需求，以使新兴行业能够及时应对能源，国家安全，医疗保健和其他领域的挑战。 从历史上看，新颖的材料探索一直缓慢而昂贵，从概念到商业化平均需要18年。 该项目通过光谱验证了材料和化学系统的主要科学挑战 - 可以通过积累，组织以及实验和计算资源和数据自动化来大大增强和扩展材料研究。 当前，大量时间用于对光谱数据的解释和理解，因为没有资源有效地完成这些任务。该项目允许在光谱领域工作的材料研究人员和化学家访问现有参数和光谱的可搜索数据库，以进行比较，自动化识别，并解决数据元素的全部数据元素 - 生产，策展，分析，分析，传播和共享。 由此产生的数据资源有助于更广泛的材料，化学和工程界的网络基础设施，并有可能催化新材料的发现以及在科学和工业中对材料和化学系统的创新使用。本地光谱数据基础架构（LSDI）项目的目标是建立基于基础基准计算光谱的第一个计算本地原子环境光谱数据库，以启用一种用于快速材料表征的在线资源，以加速材料开发和优化。通过涉及缺陷，接口和表面的纳米级工程的新技术进步，确定材料中局部原子环境已经变得越来越重要。光谱技术 - 包括X射线吸收在边缘光谱（XANES），扩展的X射线吸收精细结构（EXAFS），电子能量损耗光谱（EELS）和核磁共振（NMR） - 已成为阐明原子尺度化学结构的基本特性工具。通过利用计算方法的最新进展来填充由固态X射线吸收和NMR光谱和相关参数组成的交互式数据库，从而为所有研究人员（包括非专家）提供了越来越多的需求来帮助所有研究人员（包括非专家）进行光谱分配。该项目包括：（i）创建XAS/NMR Spectra的第一原理计算的坚固，基准的工作流程； （ii）数据生成，策划和存储； （iii）自动光谱分析算法的开发； （iv）通过材料项目传播； （v）通过新材料数据云（MDCLOUD）环境与社区的动态互动。该项目开发的数据基础架构将允许记录一个实验频谱的研究人员，例如通过NMR，XANES或XAFS的固态材料（即使是具有无序或非晶体结构）的材料的研究人员，可以通过互联网访问与相对，自动识别的现有参数的可搜索数据库，并与构造相关的型号以及相关的计算范围以及相关的计算范围以及相关的型号。 LSDI有助于材料，化学和工程社区的网络基础设施，并支持对光谱方法，材料和化学系统的基本了解的进步。该系统催化了新材料的发现，并支持材料和化学系统在科学和工业中的创新使用，符合材料基因组计划的目标。该奖项由NSF工程局（NSF工程局（NSF）工程局（民用，机械和制造创新部）和NSF材料研究局（NSF）和材料研究局（NSF）和分工（Mathem and Science of Mathem and Sciences of Mathem and Sciences）（NSF工程局共同支持）。

项目成果

The 1H T1 dispersion curve of fentanyl citrate to identify NQR parameters

使用柠檬酸芬太尼的 1H T1 离散曲线来确定 NQR 参数

• DOI: 10.1016/j.ssnmr.2020.101697
• 发表时间: 2020
• 期刊: Solid State Nuclear Magnetic Resonance
• 影响因子: 3.2
• 作者: Malone, Michael W.;Espy, Michelle A.;He, Sun;Janicke, Michael T.;Williams, Robert F.
• 通讯作者: Williams, Robert F.

A disordered rock salt anode for fast-charging lithium-ion batteries

• DOI: 10.1038/s41586-020-2637-6
• 发表时间: 2020-09-03
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Liu, Haodong;Zhu, Zhuoying;Liu, Ping
• 通讯作者: Liu, Ping

Enabling materials informatics for 29Si solid-state NMR of crystalline materials

为晶体材料的 29Si 固态 NMR 提供材料信息学支持

• DOI: 10.1038/s41524-020-0328-3
• 发表时间: 2020
• 期刊: npj Computational Materials
• 影响因子: 9.7
• 作者: Sun, He;Dwaraknath, Shyam;Ling, Handong;Qu, Xiaohui;Huck, Patrick;Persson, Kristin A.;Hayes, Sophia E.
• 通讯作者: Hayes, Sophia E.

Influence of Alkali Metal Cations on the Photodimerization of Bromo Cinnamates Studied by Solid-State NMR

固态核磁共振研究碱金属阳离子对溴代肉桂酸酯光二聚反应的影响

• DOI: 10.1021/acs.jpcc.0c09826
• 发表时间: 2020
• 期刊: The Journal of Physical Chemistry C
• 作者: Zahan, Marufa;Sun, He;Hayes, Sophia E.;Krautscheid, Harald;Haase, Jürgen;Bertmer, Marko
• 通讯作者: Bertmer, Marko

Automated generation and ensemble-learned matching of X-ray absorption spectra

• DOI: 10.1038/s41524-018-0067-x
• 发表时间: 2017-11
• 期刊: npj Computational Materials
• 影响因子: 9.7
• 作者: Chen Zheng;K. Mathew;Chi Chen;Yiming Chen;Hanmei Tang;A. Dozier;J. Kas;F. Vila;J. Rehr;L. Piper;K. Persson;S. Ong