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http://hdl.handle.net/123456789/1867
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DC Field | Value | Language |
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dc.contributor.author | Singh, Ojas | - |
dc.date.accessioned | 2022-12-14T05:16:29Z | - |
dc.date.available | 2022-12-14T05:16:29Z | - |
dc.date.issued | 2022-04 | - |
dc.identifier.uri | http://hdl.handle.net/123456789/1867 | - |
dc.description.abstract | In order to account for the missing electron correlation from the Hartree- Fock method, the Configuration Interaction method uses a variational wave function that is a linear combination of configuration state functions (CSFs) built from spin orbitals. Full CI scales unfavorably with the number of or- bitals and electrons relative to other orbital methods. Hence, implementa- tion of CI is done in Rust using numerous Code Optimizations, making this implementation extremely efficient. To deal with an even bigger systems, se- lected Configuration Interaction is explored, and a very promising Reinforce- ment Learning-based method has been implemented and improved further for excited states energies. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | IISER Mohali | en_US |
dc.subject | configuration | en_US |
dc.subject | reinforcement learning | en_US |
dc.subject | selected CI | en_US |
dc.title | Efficient implementation of configuration interaction and selected CI Using reinforcement learning | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | MS-16 |
Files in This Item:
File | Description | Size | Format | |
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It is under embargo period.pdf | 139.68 kB | Adobe PDF | View/Open |
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