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Award Detail

Doing Business As Name:Cornell University
  • Eun-Ah Kim
  • (607) 255-5014
  • Kilian Weinberger
  • Andrew G Wilson
Award Date:09/15/2019
Estimated Total Award Amount: $ 1,211,285
Funds Obligated to Date: $ 583,531
  • FY 2019=$583,531
Start Date:09/01/2019
End Date:08/31/2021
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research: Understanding Subatomic-Scale Quantum Matter Data Using Machine Learning Tools
Federal Award ID Number:1934714
DUNS ID:872612445
Parent DUNS ID:002254837
Program:HDR-Harnessing the Data Revolu
Program Officer:
  • Daryl Hess
  • (703) 292-4942

Awardee Location

Street:373 Pine Tree Road
Awardee Cong. District:23

Primary Place of Performance

Organization Name:Cornell University
Street:142 Sciences Drive
Cong. District:23

Abstract at Time of Award

A central goal of modern quantum physics is to search for new systems and technological paradigms that utilize quantum mechanical aspects of matter rather than being limited by them. In particular, there is an active search for new materials that exhibit surprising physical properties because of strong interaction between individual electrons that leads to strong correlations in the motion of electrons and as a result, to strongly correlated quantum matter. The study of Strongly Correlated Quantum Matter (SCQM) has reached a tipping point through intense efforts over the last decade that have led to vast quantities of experimental data. The next breakthrough in the field will come from relating these experimental data to theoretical models using tools of data science. However, data-driven challenges in SCQM require a fundamentally new data science approaches for two reasons: first, quantum mechanical imaging is probabilistic; and second, inference from data should be subject to fundamental laws of physics. Hence the new data-driven challenges in the field of SCQM requires "Growing Convergent Research" and "Harnessing the Data Revolution", two of NSF's Ten Big Ideas. The objective of the project is to develop and disseminate machine learning (ML) tools that can serve as a two-way highway connecting the data revolution in SCQM experiments at sub-atomic scale to a fundamental theoretical understanding of SCQM. The specific goals are: (1) Develop interpretable ML tools for position space image data; (2) Develop unsupervised ML tools for momentum space scattering data; (3) Design new imaging modality guided by the insight gained from ML; and (4) Integrate ML tools with in-operando human interface to the Cornell High Energy Synchrotron Source (CHESS) beamline. Goals (1) and (2) are within reach, while (3) and (4) are more ambitious visions for scaling up to a future institute that can involve more academic institutions and scattering experiment facilities nationwide. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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