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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.24807 |
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| _version_ | 1866913867955699712 |
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| author | Athavale, Vishikh Fedik, Nikita Colglazier, William Niklasson, Anders M. N. Kulichenko, Maksim Tretiak, Sergei |
| author_facet | Athavale, Vishikh Fedik, Nikita Colglazier, William Niklasson, Anders M. N. Kulichenko, Maksim Tretiak, Sergei |
| contents | We report the implementation of electronic excited states for semi-empirical quantum chemical methods at the configuration interaction singles (CIS) and time-dependent Hartree-Fock (TDHF) level of theory in the PySEQM software. Built on PyTorch, this implementation leverages GPU acceleration to significantly speed up molecular property calculations. Benchmark tests demonstrate that our approach can compute excited states for molecules with nearly a thousand atoms in under a minute. Additionally, the implementation also includes a machine learning interface to enable parameters re-optimization and neural network training for future machine learning applications for excited state dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24807 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PySEQM 2.0: Accelerated Semiempirical Excited State Calculations on Graphical Processing Units Athavale, Vishikh Fedik, Nikita Colglazier, William Niklasson, Anders M. N. Kulichenko, Maksim Tretiak, Sergei Chemical Physics Computational Physics We report the implementation of electronic excited states for semi-empirical quantum chemical methods at the configuration interaction singles (CIS) and time-dependent Hartree-Fock (TDHF) level of theory in the PySEQM software. Built on PyTorch, this implementation leverages GPU acceleration to significantly speed up molecular property calculations. Benchmark tests demonstrate that our approach can compute excited states for molecules with nearly a thousand atoms in under a minute. Additionally, the implementation also includes a machine learning interface to enable parameters re-optimization and neural network training for future machine learning applications for excited state dynamics. |
| title | PySEQM 2.0: Accelerated Semiempirical Excited State Calculations on Graphical Processing Units |
| topic | Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2505.24807 |