Abstract
We introduce the concept of sparse stochastic compression, an efficient stochastic sampling of any general function. The technique uses sparse stochastic orbitals (SSOs), short vectors that sample a small number of space points. As a first demonstration, SSOs are applied in conjunction with simple direct projection to accelerate our recent stochastic GW technique; the new developments enable accurate prediction of G0W0 quasiparticle energies and gaps for systems with up to Ne>10,000 electrons, with small statistical errors of ±0.05eV and using less than 2000 core CPU hours. Overall, stochastic GW scales now linearly (and often sublinearly) with Ne.
| Original language | American English |
|---|---|
| Article number | 075107 |
| Journal | Physical Review B |
| Volume | 98 |
| Issue number | 7 |
| DOIs | |
| State | Published - 6 Aug 2018 |
Bibliographical note
Funding Information:We are grateful for support by the Center for Computational Study of Excited State Phenomena in Energy Materials (C2SEPEM) at the Lawrence Berkeley National Laboratory, which is funded by the US Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DEAC02-05CH11231 as part of the Computational Materials Sciences Program.
Funding Information:
We are grateful for support by the Center for Computational Study of Excited State Phenomena in Energy Materials (C2SEPEM) at the Lawrence Berkeley National Laboratory, which is funded by the US Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DEAC02-05CH11231 as part of the Computational Materials Sciences Program. V.V. greatly appreciates helpful discussion with Gabriel Kotliar and Mark Hybertsen. The calculations were performed as part of the XSEDE computational Project No. TG-CHE170058 [52] .
Publisher Copyright:
© 2018 American Physical Society.