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Award Abstract # 1640899
CIF21 DIBBS: EI: The Local Spectroscopy Data Infrastructure (LSDI)

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE
Initial Amendment Date: July 27, 2016
Latest Amendment Date: July 27, 2016
Award Number: 1640899
Award Instrument: Standard Grant
Program Manager: Alejandro Suarez
alsuarez@nsf.gov
 (703)292-7092
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2016
End Date: September 30, 2022 (Estimated)
Total Intended Award Amount: $3,940,400.00
Total Awarded Amount to Date: $3,940,400.00
Funds Obligated to Date: FY 2016 = $3,940,400.00
History of Investigator:
  • Kristin Persson (Principal Investigator)
    kristinpersson@berkeley.edu
  • Mark Asta (Co-Principal Investigator)
  • Sophia Hayes (Co-Principal Investigator)
  • Shyue Ping Ong (Co-Principal Investigator)
Recipient Sponsored Research Office: University of California-Berkeley
1608 4TH ST STE 201
BERKELEY
CA  US  94710-1749
(510)643-3891
Sponsor Congressional District: 12
Primary Place of Performance: University of California-Berkeley
Hearst Mining Hall
Berkeley
CA  US  94720-1774
Primary Place of Performance
Congressional District:
12
Unique Entity Identifier (UEI): GS3YEVSS12N6
Parent UEI:
NSF Program(s): DMR SHORT TERM SUPPORT,
PROJECTS,
Data Cyberinfrastructure,
CDS&E
Primary Program Source: 01001617DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7237, 7433, 7569, 8048, 8400, 9102, 9263
Program Element Code(s): 171200, 197800, 772600, 808400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

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).

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 11)
Cendejas, Austin J. and Sun, He and Hayes, Sophia E. and Kortshagen, Uwe and Thimsen, Elijah "Predicting plasma conditions necessary for synthesis of -Al 2 O 3 nanocrystals" Nanoscale , v.13 , 2021 https://doi.org/10.1039/d1nr02488d Citation Details
Chen, Yiming and Chen, Chi and Zheng, Chen and Dwaraknath, Shyam and Horton, Matthew K. and Cabana, Jordi and Rehr, John and Vinson, John and Dozier, Alan and Kas, Joshua J. and Persson, Kristin A. and Ong, Shyue Ping "Database of ab initio L-edge X-ray absorption near edge structure" Scientific Data , v.8 , 2021 https://doi.org/10.1038/s41597-021-00936-5 Citation Details
Cui, Jinlei and Olmsted, David L. and Mehta, Anil K. and Asta, Mark and Hayes, Sophia E. "NMR Crystallography: Evaluation of Hydrogen Positions in Hydromagnesite by 13 C{ 1 H} REDOR SolidState NMR and Density Functional Theory Calculation of Chemical Shielding Tensors" Angewandte Chemie International Edition , v.58 , 2019 https://doi.org/10.1002/anie.201813306 Citation Details
Liu, Haodong and Zhu, Zhuoying and Yan, Qizhang and Yu, Sicen and He, Xin and Chen, Yan and Zhang, Rui and Ma, Lu and Liu, Tongchao and Li, Matthew and Lin, Ruoqian and Chen, Yiming and Li, Yejing and Xing, Xing and Choi, Yoonjung and Gao, Lucy and Cho, H "A disordered rock salt anode for fast-charging lithium-ion batteries" Nature , v.585 , 2020 https://doi.org/10.1038/s41586-020-2637-6 Citation Details
Malone, Michael W. and Espy, Michelle A. and He, Sun and Janicke, Michael T. and Williams, Robert F. "The 1H T1 dispersion curve of fentanyl citrate to identify NQR parameters" Solid State Nuclear Magnetic Resonance , v.110 , 2020 https://doi.org/10.1016/j.ssnmr.2020.101697 Citation Details
Srivastava, Deepansh J. and Vosegaard, Thomas and Massiot, Dominique and Grandinetti, Philip J. and Ramamoorthy, Ayyalusamy "Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data" PLOS ONE , v.15 , 2020 10.1371/journal.pone.0225953 Citation Details
Sun, He and Dwaraknath, Shyam and Ling, Handong and Qu, Xiaohui and Huck, Patrick and Persson, Kristin A. and Hayes, Sophia E. "Enabling materials informatics for 29Si solid-state NMR of crystalline materials" npj Computational Materials , v.6 , 2020 https://doi.org/10.1038/s41524-020-0328-3 Citation Details
Sun, He and Hammann, Blake A. and Brady, Alexander B. and Singh, Gurpreet and Housel, Lisa M. and Takeuchi, Esther S. and Takeuchi, Kenneth J. and Marschilok, Amy C. and Hayes, Sophia E. and Szczepura, Lisa F. "Structural Investigation of Silver Vanadium Phosphorus Oxide (Ag 2 VO 2 PO 4 ) and Its Reduction Products" Chemistry of Materials , v.33 , 2021 https://doi.org/10.1021/acs.chemmater.1c00446 Citation Details
Zahan, Marufa and Sun, He and Hayes, Sophia E. and Krautscheid, Harald and Haase, Jürgen and Bertmer, Marko "Influence of Alkali Metal Cations on the Photodimerization of Bromo Cinnamates Studied by Solid-State NMR" The Journal of Physical Chemistry C , v.124 , 2020 https://doi.org/10.1021/acs.jpcc.0c09826 Citation Details
Zheng, Chen and Chen, Chi and Chen, Yiming and Ong, Shyue Ping "Random Forest Models for Accurate Identification of Coordination Environments from X-Ray Absorption Near-Edge Structure" Patterns , v.1 , 2020 10.1016/j.patter.2020.100013 Citation Details
Zheng, Chen and Mathew, Kiran and Chen, Chi and Chen, Yiming and Tang, Hanmei and Dozier, Alan and Kas, Joshua J. and Vila, Fernando D. and Rehr, John J. and Piper, Louis F. and Persson, Kristin A. and Ong, Shyue Ping "Automated generation and ensemble-learned matching of X-ray absorption spectra" npj Computational Materials , v.4 , 2018 10.1038/s41524-018-0067-x Citation Details
(Showing: 1 - 10 of 11)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The field of materials science is entering the era of the fourth paradigm of science: data-driven materials design. However, systematic, organized materials data across chemical systems and structures is still very rare, which is hampering efforts in machine learning and accelerated design. The Local Spectroscopy Data Infrastructure (LSDI) project developed computational workflows and carefully benchmarked them against experimental evidence. Using high-throughput software and data infrastructure, the workflows enabled the creation of the first computational local atomic environment spectroscopy database which now provides a publicly available, online resource for rapid material characterization, to accelerate materials development and optimization. The resulting database consists of computed x-ray absorption spectra for over 50,000 crystalline materials and nuclear magnetic resonance spectra for over 9,000 crystalline materials. This data is already being used to power fast and accurate machine learning algorithms to advance characterization tools towards understanding atomic-scale materials structure, electronic properties, and quantum phenomena. The addition of spectral data and automated search/comparison algorithms is enabling students, postdocs and engineers all over the world, irrespective of resources as long as they have access to internet, to participate in the data revolution of materials science. Through the LSDI funding, we also seeded the development of a portal for community contributions of materials data; MPContribs. This portal now powers over 50 contributed materials data projects, with provenance and connections to other materials data resources, such as the Materials Project.

 


Last Modified: 01/31/2023
Modified by: Kristin A Persson

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