NUG Community Call February 29, 2024

Date: Thursday, February 29, 2024

Time: 11:00 PST

The Monthly NUG Community Call is a regular opportunity for our users to show off what they've done, for NERSC to get feedback from users, and for users to exchange ideas.

Zoom: https://lbnl.zoom.us/j/285479463 (full connection details below). We'll also use the NERSC Users Slack #webinars channel for discussion before, during and after the meeting.

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Agenda

• Announcements and Calls for Participation: Upcoming conferences, workshops, or other events, that you think might interest or benefit the NERSC user community
• New Year Allocation Reminders
• Science Talk: Tree Testing (Annette Greiner, NERSC)
• Topic-of-the-day: Towards Large-Scale Materials Modeling at Quantum Accuracy (Bikash Kanungo)

Abstract:

Density functional theory (DFT), owing to its great balance of speed and accuracy, has remained an essential tool to understand all manners of nanoscale processes and materials behavior. Although, in principle, an exact theory, in practice, DFT requires approximations to the exact exchange-correlation (XC) functionals to encapsulate the quantum many-electron interactions into a mean-field of the electron density. The existing XC approximations remain far from the quantum accuracy of 1-5 mHa/atom, and hence, severely limit the reliability of DFT in predicting material properties. Additionally, the high computational demands of DFT limits their routine usage to length-scales of few hundred atoms or a few tens of picoseconds for ab initio molecular dynamics (AIMD). As a result, an accurate understanding of a wide array of key scientific problems, such as dislocations in metal alloys, photocatalysis, plasmonics in metal nanoclusters, charge transfer in biomolecules, etc., remain inaccessible to DFT. In this talk, I will present different strategies to address the above accuracy and efficiency challenges in DFT. I will introduce a data-driven approach to model the XC approximation. In particular, I will present an accurate and robust solution to the inverse DFT problem that connects DFT to the quantum many-body based methods, and hence, is crucial to the generation of training data needed to model the XC approximation. Next, I will discuss various machine-learning approaches to construct the XC approximation, using the training data from inverse DFT. Lastly, I will also present various numerical, algorithmic, and high-performance computing (HPC) advances that have enabled DFT calculations at length-scales that were inaccessible, heretofore.

Video Recording: 

Presentation Slides:

NUG Community Call Presentation (Feb2024)

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