Predicting the Electronic Structure of Matter on Ultra-Large Scales

Technical Report · 20 October 2022

DOI:https://doi.org/10.2172/1895024· OSTI ID:1895024

Fiedler, Lenz ^[1]; Modine, Normand ^[2]; Schmerler, Steve ^[3]; Vogel, Dayton J. ^[2]; Popoola, Gabriel A. ^[4]; Thompson, Aidan ^[2]; Rajamanickam, Sivasankaran ^[2]; Cangi, Attila ^[1]

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1. Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf, Saxony (Germany)
2. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
3. Helmholtz-Zentrum Dresden-Rossendorf, Saxony (Germany)
4. Elder Research, Inc., Charlottesville, VA (United States)

The long-standing problem of predicting the electronic structure of matter on ultra-large scales (beyond 100,000 atoms) is solved with machine learning.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: NA0003525
• OSTI ID: 1895024
• Report Number(s): SAND2022-14839R; 711389; TRN: US2309370
• Country of Publication: United States
• Language: English

Related Subjects

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION