Refactor spin-flip TDDFT/TDA including analytic gradient and NACV#731
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- Keep all initial guess vectors in the first iteration - Implement double Gram-Schmidt orthogonalization for better stability
- Add davidson_nosym1 diagonalizer in tdscf._lr_eig for Spin-flip TDDFT. - Refactor SpinFlipTDA/TDDFT classes in tdscf.uhf and add post-processing. - Update test suite: add test_sftda.py, update test_sftddft.py, and remove obsolete tests. - Apply necessary code formatting.
- Refactor analytic gradient implementations in grad/tduks_sf.py. - Add df/grad/tduks_sf.py to support density-fitting gradients. - Add for SF-TDA and SF-TDDFT analytic gradient. - Consolidate and optimize SF-TDA/TDDFT tests to reduce CI runtime. - Improve exception messages in grad/tduks.py.
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In the newest commits, spin-flip TDDFT NACV and its tests has been also pushed onto this branch. |
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This PR refactors the spin-flip TDDFT/TDA module with the following changes:
Davidson Solver: Improve stability with double Gram-Schmidt and keep all initial guess vectors in the first iteration to help convergence in
tdscf._lr_eig.eigh. Adddavidson_nosym1for SF-TDDFT.Core Refactor: Refactor
SpinFlipTDAandSpinFlipTDDFTclasses intdscf.uhf, and add necessary post-processing functions.Gradients: Refactor SF-TDDFT analytic gradients (supporting both with and without density-fitting).
Nonadiabatic Coupling Vectors: Add SF-TDDFT analytic NACV (supporting both with and without density-fitting).
Tests: Add and update tests for SF-TDDFT/TDA, their analytic gradients and NACVs.
Others: Improve some error messages and remove obsolete test files.
Note: Example input files are not included in this PR.