| |
| """MagNET predicts NMR shieldings using equivariant neural networks. |
| |
| | function | what you get | |
| |---|---| |
| | `predict_shifts` | <sup>1</sup>H and <sup>13</sup>C chemical shifts (ppm) in a specific solvent using MagNET-Zero/MagNET-PCM | |
| | `predict_shieldings` | <sup>1</sup>H and <sup>13</sup>C shieldings (ppm) in the gas phase | |
| | `implicit_solvent_correction` | shieldings(PCM=chloroform) - shieldings(gas phase) | |
| | `explicit_solvent_correction` | shieldings(solute+solvents) - shieldings(solute) | |
| |
| **Inputs and outputs** |
| |
| - One molecule: array of `atomic_numbers` (e.g. 6 for carbon) and |
| `coordinates` (an N-by-3 array of xyz positions in Angstrom, a numpy array or a nested list). You |
| get one numpy array back, one value per atom. |
| - Many molecules: pass a list of each, and you get a list of arrays back. |
| |
| **Geometries** MagNET-Zero and MagNET-PCM expect AIMNet2-optimized geometries. |
| |
| **Supported Solvents** |
| |
| | solvent | `predict_shifts` | `implicit_solvent_correction` | `explicit_solvent_correction` | |
| |---|:---:|:---:|:---:| |
| | tetrahydrofuran | ✓ | | | |
| | dichloromethane | ✓ | | | |
| | chloroform | ✓ | ✓ | ✓ | |
| | toluene | ✓ | | | |
| | benzene | ✓ | | ✓ | |
| | chlorobenzene | ✓ | | | |
| | acetone | ✓ | | | |
| | dimethylsulfoxide | ✓ | | | |
| | acetonitrile | ✓ | | | |
| | trifluoroethanol | ✓ | | | |
| | methanol | ✓ | | ✓ | |
| | water | ✓ | | ✓ | |
| |
| `implicit_solvent_correction` predicts only the chloroform correction; `predict_shifts` linearly scales it to the other solvents. |
| |
| **Shared options** (all four functions): |
| |
| - `n_passes` (default `10`): average out equivariance error over `n_passes` forward passes |
| - `symmetrize` (default `True`): if True, average over `n_passes` on the input geometry and `n_passes` on the mirror image of the input geometry |
| - `device` (default `None`): where to run, a torch device or a string like `"cpu"` or `"cuda"`; uses GPU if available |
| |
| """ |
| __docformat__ = "google" |
|
|
| from .api import (predict_shifts, predict_shieldings, implicit_solvent_correction, |
| explicit_solvent_correction) |
|
|
| __all__ = ["predict_shifts", "predict_shieldings", "implicit_solvent_correction", |
| "explicit_solvent_correction"] |
|
|
