StochasticGW-GPU: rapid quasi-particle energies for molecules beyond 10000 atoms
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911449378455552 |
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| author | Thomas, Phillip S. Nguyen, Minh Bazile, Dimitri Allen, Tucker Li, Barry Y. Li, Wenfei Del Ben, Mauro Deslippe, Jack Neuhauser, Daniel |
| author_facet | Thomas, Phillip S. Nguyen, Minh Bazile, Dimitri Allen, Tucker Li, Barry Y. Li, Wenfei Del Ben, Mauro Deslippe, Jack Neuhauser, Daniel |
| contents | $\mathtt{StochasticGW}$ is a code for computing accurate Quasi-Particle (QP) energies of molecules and material systems in the GW approximation. $\mathtt{StochasticGW}$ utilizes the stochastic Resolution of the Identity (sROI) technique to enable a massively-parallel implementation with computational costs that scale semi-linearly with system size, allowing the method to access systems with tens of thousands of electrons. We introduce a new implementation, $\mathtt{StochasticGW-GPU}$, for which the main bottleneck steps have been ported to GPUs and which gives substantial performance improvements over previous versions of the code. We showcase the new code by computing band gaps of hydrogenated silicon clusters ($\textrm{S}\textrm{i}_{\textrm{x}}\textrm{H}_{\textrm{y}}$) containing up to 10001 atoms and 35144 electrons, and we obtain individual QP energies with a statistical precision of better than $\pm0.03$ eV with times-to-solution on the order of minutes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21005 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | StochasticGW-GPU: rapid quasi-particle energies for molecules beyond 10000 atoms Thomas, Phillip S. Nguyen, Minh Bazile, Dimitri Allen, Tucker Li, Barry Y. Li, Wenfei Del Ben, Mauro Deslippe, Jack Neuhauser, Daniel Chemical Physics Materials Science $\mathtt{StochasticGW}$ is a code for computing accurate Quasi-Particle (QP) energies of molecules and material systems in the GW approximation. $\mathtt{StochasticGW}$ utilizes the stochastic Resolution of the Identity (sROI) technique to enable a massively-parallel implementation with computational costs that scale semi-linearly with system size, allowing the method to access systems with tens of thousands of electrons. We introduce a new implementation, $\mathtt{StochasticGW-GPU}$, for which the main bottleneck steps have been ported to GPUs and which gives substantial performance improvements over previous versions of the code. We showcase the new code by computing band gaps of hydrogenated silicon clusters ($\textrm{S}\textrm{i}_{\textrm{x}}\textrm{H}_{\textrm{y}}$) containing up to 10001 atoms and 35144 electrons, and we obtain individual QP energies with a statistical precision of better than $\pm0.03$ eV with times-to-solution on the order of minutes. |
| title | StochasticGW-GPU: rapid quasi-particle energies for molecules beyond 10000 atoms |
| topic | Chemical Physics Materials Science |
| url | https://arxiv.org/abs/2601.21005 |