Efficient All-electron Hybrid Density Functionals for Atomistic Simulations Beyond 10,000 Atoms (2403.10343v1)
Abstract: Hybrid density functional approximations (DFAs) offer compelling accuracy for ab initio electronic-structure simulations of molecules, nanosystems, and bulk materials, addressing some deficiencies of computationally cheaper, frequently used semilocal DFAs. However, the computational bottleneck of hybrid DFAs is the evaluation of the non-local exact exchange contribution, which is the limiting factor for the application of the method for large-scale simulations. In this work, we present a drastically optimized resolution-of-identity-based real-space implementation of the exact exchange evaluation for both non-periodic and periodic boundary conditions in the all-electron code FHI-aims, targeting high-performance CPU compute clusters. The introduction of several new refined Message Passing Interface (MPI) parallelization layers and shared memory arrays according to the MPI-3 standard were the key components of the optimization. We demonstrate significant improvements of memory and performance efficiency, scalability, and workload distribution, extending the reach of hybrid DFAs to simulation sizes beyond ten thousand atoms. As a necessary byproduct of this work, other code parts in FHI-aims have been optimized as well, e.g., the computation of the Hartree potential and the evaluation of the force and stress components. We benchmark the performance and scaling of the hybrid DFA based simulations for a broad range of chemical systems, including hybrid organic-inorganic perovskites, organic crystals and ice crystals with up to 30,576 atoms (101,920 electrons described by 244,608 basis functions).
- L. Goerigk and S. Grimme, Journal of Chemical Theory and Computation 6, 107 (2010).
- A. D. Becke, The Journal of Chemical Physics 98, 5648 (1993), https://pubs.aip.org/aip/jcp/article-pdf/98/7/5648/11091662/5648_1_online.pdf .
- M. Ernzerhof and G. E. Scuseria, The Journal of chemical physics 110, 5029 (1999).
- A. J. Garza and G. E. Scuseria, The journal of physical chemistry letters 7, 4165 (2016).
- S. Lany and A. Zunger, Phys. Rev. B 81, 205209 (2010a).
- S. Lany and A. Zunger, Phys. Rev. B 81, 205209 (2010b).
- O. A. Vydrov and G. E. Scuseria, The Journal of Chemical Physics 125, 234109 (2006), https://pubs.aip.org/aip/jcp/article-pdf/doi/10.1063/1.2409292/15391055/234109_1_online.pdf .
- R. Peverati and D. G. Truhlar, The Journal of Physical Chemistry Letters 2, 2810 (2011).
- J.-D. Chai and M. Head-Gordon, The Journal of Chemical Physics 128, 084106 (2008), https://pubs.aip.org/aip/jcp/article-pdf/doi/10.1063/1.2834918/15411250/084106_1_online.pdf .
- C. Adamo and V. Barone, The Journal of chemical physics 110, 6158 (1999).
- A. Bussy and J. Hutter, The Journal of Chemical Physics 160, 064116 (2024), https://pubs.aip.org/aip/jcp/article-pdf/doi/10.1063/5.0189659/19669893/064116_1_5.0189659.pdf .
- A. Förster and L. Visscher, Journal of Computational Chemistry 41, 1660 (2020).
- L. Lin, Journal of chemical theory and computation 12, 2242 (2016).
- P. Pulay, Chemical Physics Letters 73, 393 (1980).
- Max Planck Computing and Data Facility, “Raven User Guide,” (2023), [Online; accessed 19-June-2023].
- J. D. McCalpin, IEEE Computer Society Technical Committee on Computer Architecture (TCCA) Newsletter , 19 (1995).
- “Fe2o3 crystal structure: Datasheet from “pauling file multinaries edition – 2022” in springermaterials (https://materials.springer.com/isp/crystallographic/docs/sd_0314193),” (a), copyright 2023 Springer-Verlag Berlin Heidelberg & Material Phases Data System (MPDS), Switzerland & National Institute for Materials Science (NIMS), Japan.
- “Fe2sio4 (fe2[sio4]) crystal structure: Datasheet from “pauling file multinaries edition – 2022” in springermaterials (https://materials.springer.com/isp/crystallographic/docs/sd_0375064),” (b), copyright 2023 Springer-Verlag Berlin Heidelberg & Material Phases Data System (MPDS), Switzerland & National Institute for Materials Science (NIMS), Japan.