NSF award 2544469 · University of California-Berkeley

CAREER: Machine Learning Methods for Accelerating Large-Scale Quantum Mechanical Simulations

Final report due

28 October 2031

in 1,836 days · The award ends on 30 June 2031.

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Decide about an extension2 Mar 2031in 1596 days
award ending 2031-06-30 · Research.gov
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Extension request16 May 2031in 1671 days
award ending 2031-06-30 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Jun 2031in 1706 days
award ending 2031-06-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Oct 2031in 1836 days
award ending 2031-06-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Oct 2031in 1836 days
award ending 2031-06-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Oct 2031in 1836 days
award ending 2031-06-30 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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NSF lists this award's end date as 30 June 2031.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 October 2031.

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Award card

Award number: 2544469
Period of support: 1 Jul 2026 – 30 Jun 2031
Amount: $609,805
Awardee: University of California-Berkeley, Berkeley, CA
Program: Info Integration & Informatics
Directorate: CSE

Abstract

Modern scientific progress in areas such as drug discovery and energy depends on the ability to accurately predict how matter behaves at the atomic level. This project aims to bridge the gap between small-scale theoretical models and the large-scale simulations required to design real-world technologies. By developing new machine learning tools that model physical systems with much greater efficiency, this research…

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