Stochastic Density Functional Theory: Real- and Energy-Space Fragmentation for Noise Reduction
ORAL · Invited
Abstract
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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Publication: M. Chen, D. Neuhauser, R. Baer, and E. Rabani. "Geometry Optimization of Materials with Stochastic Density Functional Theory", in preparation
M. Chen, D. Neuhauser, R. Baer, and E. Rabani. "Stochastic Density Functional Theory: Real- and Energy-Space Fragmentation for Noise Reduction." J. Chem. Phys. 154, 204108 (2021).
M. Chen, D. Neuhauser, R. Baer, and E. Rabani. "Energy Window Stochastic Density Functional Theory." J. Chem. Phys. 151, 114116 (2019).
M. Chen, D. Neuhauser, R. Baer, and E. Rabani. "Overlapped Embedded Fragment Stochastic Density Functional Theory for Covalently Bonded Materials." J. Chem. Phys. 150, 034106 (2019).
Presenters
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Ming Chen
- Purdue U