
NSF Org: |
OAC Office of Advanced Cyberinfrastructure (OAC) |
Recipient: |
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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: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1608 4TH ST STE 201 BERKELEY CA US 94710-1749 (510)643-3891 |
Sponsor Congressional District: |
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Primary Place of Performance: |
Hearst Mining Hall Berkeley CA US 94720-1774 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
DMR SHORT TERM SUPPORT, PROJECTS, Data Cyberinfrastructure, CDS&E |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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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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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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