Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session F46: Real Space Methods for the Electronic Structure Problem
8:00 AM–11:00 AM,
Tuesday, March 15, 2022
Room: McCormick Place W-470A
Sponsoring Unit: DCOMP
Chair: Mehmet Dogan, University of California, Berkeley
Abstract: F46.00001 : Stochastic Density Functional Theory: Real- and Energy-Space Fragmentation for Noise Reduction*
8:00 AM–8:36 AM
(University of California, Berkeley)
(Fritz Haber Center of Molecular Dynamics and Institute of Chemistry, The Hebrew University of Jerusalem)
(Department of Chemistry and Biochemistry, University of California, Los Angeles)
Real-space/plane-wave based density function theory (DFT) is an important approach to understand electronic, optical, and magnetic properties of semiconductor and metallic materials. However, the computational scaling of conventional DFT methods is relatively high. Such a high scaling limits DFT applications in modeling complex materials such as semiconductor/metallic devices and nanomaterials. Therefore, developing linear scaling DFT methods is necessary for studying complex materials. While most linear scaling DFT assumes a localized density matrix, this assumption is not required for stochastic DFT method which utilizes stochastic orbitals instead of deterministic Kohn-Sham orbitals. However, noise in stochastic DFT limits the efficiency of this approach. Various noise reduction techniques that use fragmentations in real space and/or energy space have been developed. These techniques can significantly reduce the noise level in stochastic DFT to enhance the computational efficiency. These noise-reduction stochastic DFT methods have been applied to geometry optimization of semiconductor materials which is a challenging problem for stochastic DFT with noisy atomic forces.
*The speaker would like to ackowledge support from the Center for Computational Study of Excited State Phenomena in Energy Materials (C2SEPEM) at the Lawrence Berkeley National Laboratory, which is funded by the U.S. Department of Energy and the startup funding support at Purdue University. The speaker also want to acknowledge the computational resources provided by the National Energy Research Scientific Computing Center (NERSC).
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