We lifted 8 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| schnarc/schnarc | canonical | 5 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| get_diab | Ran | schnarc/schnarc/src/schnarc/schnarc.py code served (permissive licence) · get_code("772cc49a755745ff") |
| get_socs | Ran | schnarc/schnarc/src/schnarc/schnarc.py code served (permissive licence) · get_code("a989d6462566487a") |
| phaseless_loss | Ran | schnarc/schnarc/src/schnarc/nn.py code served (permissive licence) · get_code("2a8a3609011f9f81") |
| read_tradeoffs | Ran | schnarc/schnarc/src/schnarc/utils.py code served (permissive licence) · get_code("85d12cfff8578b15") |
| read_zmat | Ran | schnarc/schnarc/src/schnarc/utils.py code served (permissive licence) · get_code("3030ee01ab9feac8") |
| diagonal_phaseloss | Not yet run | schnarc/schnarc/src/schnarc/nn.py code served (permissive licence) · get_code("a969d8293fb86775") |
| get_schnarc | Not yet run | schnarc/schnarc/src/schnarc/schnarc.py code served (permissive licence) · get_code("f1138a9ae711e193") |
| read_QMout | Not yet run | schnarc/schnarc/src/schnarc/utils.py code served (permissive licence) · get_code("bdeef284ceabe32e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties for photodynamics simulations. The properties are multiple energies, forces, nonadiabatic couplings and spin-orbit couplings. The nonadiabatic couplings are learned in a phase-free manner as derivatives of a virtually constructed property by the deep learning model, which guarantees rotational covariance. Additionally, an approximation for nonadiabatic couplings is introduced, based on the potentials, their gradients and Hessians. As deep-learning method, we employ SchNet extended for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on a model system and two realistic polyatomic molecules and paves the way towards efficient photodynamics simulations of complex systems.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2002.07264")
get_code_for_paper("2002.07264")
have("2002.07264")
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