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For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/README.md b/README.md new file mode 100644 index 0000000..cba1359 --- /dev/null +++ b/README.md @@ -0,0 +1,65 @@ +# LnQM dataset + +[![Python](https://img.shields.io/badge/python-3.11.4-blue.svg)](https://www.python.org) +[![code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) + +This is the offical repository of the `LnQM` dataset developed by the Grimme group in Bonn. + +
+LnQM Dataset +
+ + +# Data + +The data is available under: [https://zenodo.org/records/10222384](https://zenodo.org/records/10222384) + + +# Setup + +We provide a dataset class and a sample representation based on `pytorch`. +For this purpose please install the package requirements: +```bash +conda env create --file requirements.yml +``` + +Basic usage of the dataset: +```python +from lnqm import LnQM_Dataset + +# load LnQM from disk +dataset = LnQM_Dataset(path_to_hdf5="/path/to/lnqm.h5") + +# loop over each sample in the dataset +for data in dataset: + # ... + # do sth. with each sample + # ... + + # e.g. access properties + print(data.uid) +``` + +For a more detailed description of the features included see the datamodel in `LnQM_Sample`. +If required, a `Data` object can be converted via: + +```python +from lnqm import LnQM_Sample +sample = LnQM_Sample(**data) +``` + +# Citations + +When using or referencing to the `LnQM` please cite: +- C. Hölzer, I. Gordiy, S. Grimme, M. Bursch + ... tbd ... + +# License + +[![CC BY NC 4.0][cc-by-nc-image]][cc-by-nc] + +This work is licensed under a +[Creative Commons Attribution-NonCommercial 4.0 International License][cc-by-nc]. + +[cc-by-nc]: http://creativecommons.org/licenses/by-nc/4.0/ +[cc-by-nc-image]: https://i.creativecommons.org/l/by-nc/4.0/88x31.png \ No newline at end of file diff --git a/assets/logo.png b/assets/logo.png new file mode 100644 index 0000000..a21191a Binary files /dev/null and b/assets/logo.png differ diff --git a/lnqm/__init__.py b/lnqm/__init__.py new file mode 100644 index 0000000..dcc3594 --- /dev/null +++ b/lnqm/__init__.py @@ -0,0 +1,2 @@ +from .dataset import LnQM_Dataset +from .sample import LnQM_Sample diff --git a/lnqm/dataset.py b/lnqm/dataset.py new file mode 100644 index 0000000..a5ccf91 --- /dev/null +++ b/lnqm/dataset.py @@ -0,0 +1,68 @@ +from collections import defaultdict +from pathlib import Path + +import h5py +import numpy as np +import torch +from torch_geometric.data import Data, InMemoryDataset + + +class LnQM_Dataset(InMemoryDataset): + """Dataset holding LnQM data. + + Usage: + >>> dataset = LnQM_Dataset(path_to_hdf5="lnqm.h5") + """ + + def __init__(self, path_to_hdf5: str | Path, transform=None): + super().__init__("./", transform, pre_transform=None, pre_filter=None) + self.path_to_hdf5 = path_to_hdf5 + + if self.path_to_hdf5: + self.data, self.slices = LnQM_Dataset.from_hdf5(self.path_to_hdf5) + else: + # empty dataset + self.data, self.slices = Data(), defaultdict(dict, {}) + + @staticmethod + def from_hdf5(fp: str | Path) -> tuple[Data, defaultdict]: + """Load data and slices from HDF5 file.""" + data = {} + slices = {} + with h5py.File(fp, "r") as f: + for key in f["data"].keys(): + np_arrays = {"data": f["data"][key][:], "slices": f["slices"][key][:]} + # some casting + for prop, val in np_arrays.items(): + if val.dtype == np.uint64: + np_arrays[prop] = val.astype(np.int64) + # uid is of dtype string, so we got to handle it seperately + if key == "uid": + uids = [s.decode("utf-8") for s in np_arrays["data"].tolist()] + data[key] = uids + slices[key] = torch.from_numpy(np_arrays["slices"]) + else: + data[key] = torch.from_numpy(np_arrays["data"]) + slices[key] = torch.from_numpy(np_arrays["slices"]) + return Data.from_dict(data), defaultdict(dict, slices) + + def to_hdf5(self, fp: str | Path): + """Save the data and slices of the dataset to an HDF5 file.""" + with h5py.File(fp, "w") as f: + data_group = f.create_group("data") + slices_group = f.create_group("slices") + + for key, value in self._data.items(): + if not isinstance(value, list): # strings such as uid + if isinstance(value, torch.Tensor): + value = value.numpy() + if value.dtype == np.int64: + value = value.astype(np.uint64) + + data_group.create_dataset(key, data=value) + + # save slices + slice_value = self.slices[key].numpy() + if slice_value.dtype == np.int64: + slice_value = slice_value.astype(np.uint64) + slices_group.create_dataset(key, data=slice_value) diff --git a/lnqm/sample.py b/lnqm/sample.py new file mode 100644 index 0000000..56deabe --- /dev/null +++ b/lnqm/sample.py @@ -0,0 +1,196 @@ +from pydantic import BaseModel +from torch import Tensor + + +class LnQM_Sample(BaseModel): + """ + Data model representation for samples in the `LnQM` dataset. + + While designed for comprehensive representation, some attributes + may contain redundant information. For instance: + - Total electron count (`nel`) can be deduced from alpha (`nel_alpha`) + and beta (`nel_beta`) electron counts. + - The combined exchange-correlation energy (`exc`) is the sum of + exchange (`ex`) and correlation (`ec`) energies. + - The Mulliken charges are encompassed within Mayer population. + + Units: + All numerical attributes are expressed in atomic units (Hartree, Bohr, etc.). + """ + + class Config: + arbitrary_types_allowed = True + + # General properties + uid: str + """Unique identifier for the calculation.""" + + time_singlepoint: Tensor + """Time taken for the singlepoint ORCA calculation in seconds.""" + + time_geoopt: Tensor + """Time taken for the geometry optimization ORCA calculation in seconds.""" + + # Geometric properties + numbers: Tensor + """Atomic numbers.""" + + coord: Tensor + """Optimized geometry in Bohr.""" + + gradient: Tensor + """Gradient of the optimized geometry in Eh/Bohr.""" + + trajectory: Tensor + """Trajectory of atomic positions during optimization in Bohr.""" + + trajectory_energies: Tensor + """Energies at different points in the optimization trajectory in Eh.""" + + trajectory_etot: Tensor + """Energies without dispersion correction at different points in the optimization trajectory in Eh.""" + + trajectory_gradients: Tensor + """Gradients at different points in the optimization trajectory in Eh/Bohr.""" + + cn: Tensor + """Coordination numbers from D4 calculation.""" + + # Energetic properties + energy: Tensor + """Total single point energy from the ORCA calculation in Eh.""" + + energy_geoopt: Tensor + """Energy after geometric optimization in Eh. This is important for comparability with other trajectory features.""" + + etot_geoopt: Tensor + """Energy after geometric optimization without dispersion correction in Eh. This is important for comparability with other trajectory features.""" + + ex: Tensor + """Exchange energy in Eh.""" + + ec: Tensor + """Correlation energy in Eh.""" + + exc: Tensor + """Exchange-correlation energy in Eh.""" + + ecnl: Tensor + """Non-local correlation energy in Eh.""" + + eemb: Tensor + """Embedding correction energy in Eh.""" + + homo_spin_up: Tensor + """HOMO energy for spin-up electrons in Eh.""" + # NOTE: SOMO for alpha spin channel in open shell systems + + lumo_spin_up: Tensor + """LUMO energy for spin-up electrons in Eh.""" + + homo_spin_down: Tensor + """HOMO energy for spin-down electrons in Eh.""" + # NOTE: SOMO for beta spin channel in open shell systems + + lumo_spin_down: Tensor + """LUMO energy for spin-down electrons in Eh.""" + + orbital_energies_spin_up: Tensor + """Energies of orbitals for spin-up electrons in Eh.""" + + orbital_energies_spin_down: Tensor + """Energies of orbitals for spin-down electrons in Eh.""" + + # Electronic properties + charge: Tensor + """Total charge of the molecule in e.""" + + unpaired_e: Tensor + """Number of unpaired electrons.""" + + nel_alpha: Tensor + """Number of alpha electrons.""" + + nel_beta: Tensor + """Number of beta electrons.""" + + nel: Tensor + """Total number of electrons.""" + + polarizabilities: Tensor + """Polarizabilities from D4 calculation.""" + + eeq: Tensor + """EEQ charges from tblite calculation in e.""" + + ceh: Tensor + """CEH charges from tblite calculation in e.""" + + q_gfn2: Tensor + """Charges from GFN2 calculation in e.""" + + # Population and bond properties + mayer_pop: Tensor + """Mayer population analysis values. + Sorted by: + NA - Mulliken gross atomic population + ZA - Total nuclear charge + QA - Mulliken gross atomic charge + VA - Mayer's total valence + BVA - Mayer's bonded valence + FA - Mayer's free valence + """ + + mayer_bo: Tensor + """Mayer bond order values with tuple indices. + Sorted by: + Atom idx A - idx of atom A + Atom idx B - idx of atom B + Bond order - value of bond order + """ + + loewdin_charges: Tensor + """Loewdin atomic charges.""" + + loewdin_spins: Tensor + """Loewdin spin populations.""" + + mulliken_charges: Tensor + """Mulliken atomic charges.""" + + mulliken_spins: Tensor + """Mulliken spin populations.""" + + hirshfeld_charges: Tensor + """Hirshfeld atomic charges.""" + + hirshfeld_spins: Tensor + """Hirshfeld spin populations.""" + + hirshfeld_alpha: Tensor + """Hirshfeld total integrated alpha density.""" + + hirshfeld_beta: Tensor + """Hirshfeld total integrated beta density.""" + + # Molecular properties + rot_const: Tensor + """Rotational constants in MHz.""" + + rot_dipole: Tensor + """Dipole components along the rotational axes in A.U..""" + + dipole: Tensor + """Magnitude of the dipole moment in A.U..""" + + dipole_ele: Tensor + """Electronic xyz-contribution to the dipole moment.""" + + dipole_nuc: Tensor + """Nuclear xyz-contribution to the dipole moment.""" + + dipole_tot: Tensor + """Total xyz-dipole moment.""" + + def __str__(self): + return f"Sample({self.uid})" diff --git a/requirements.yml b/requirements.yml new file mode 100644 index 0000000..1d73972 --- /dev/null +++ b/requirements.yml @@ -0,0 +1,13 @@ +name: lnqm +channels: + - conda-forge + - defaults + - pyg +dependencies: + - python=3.11.4 + - h5py + - pytorch + - pydantic + - pip + - pip: + - torch_geometric \ No newline at end of file