\n",
"\n",
"I. Property Unit Description\n",
"-- -------- ----------- --------------\n",
" 1 tag - \"gdb9\"; string constant to ease extraction via grep\n",
" 2 index - Consecutive, 1-based integer identifier of molecule\n",
" 3 A GHz Rotational constant A\n",
" 4 B GHz Rotational constant B\n",
" 5 C GHz Rotational constant C\n",
" 6 mu Debye Dipole moment\n",
" 7 alpha Bohr^3 Isotropic polarizability\n",
" 8 homo Hartree Energy of Highest occupied molecular orbital (HOMO)\n",
" 9 lumo Hartree Energy of Lowest occupied molecular orbital (LUMO)\n",
"10 gap Hartree Gap, difference between LUMO and HOMO\n",
"11 r2 Bohr^2 Electronic spatial extent\n",
"12 zpve Hartree Zero point vibrational energy\n",
"13 U0 Hartree Internal energy at 0 K\n",
"14 U Hartree Internal energy at 298.15 K\n",
"15 H Hartree Enthalpy at 298.15 K\n",
"16 G Hartree Free energy at 298.15 K\n",
"17 Cv cal/(mol K) Heat capacity at 298.15 K\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preprocessing dataset\n",
"\n",
"Dataset extraction depends on the preprocessing method, which is determined by `preprocessor`.\n",
"\n",
"Here, let's look an example of using `GGNNPreprocessor` preprocessor for QM9 dataset extraction.\n",
"\n",
"Procedure is as follows,\n",
"\n",
"1. Instantiate `preprocessor` (here `GGNNPreprocessor` is used).\n",
"2. call `get_qm9` method with `preprocessor`.\n",
" - `labels=None` option is used to extract all labels. In this case, 15 types of physical properties are extracted (see above).\n",
"\n",
"Note that `return_smiles` option can be used to get SMILES information together with the dataset itself."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████████████████████████████████████████████████████████████████| 133885/133885 [01:35<00:00, 1406.47it/s]\n",
"INFO:chainer_chemistry.dataset.parsers.csv_file_parser:Preprocess finished. FAIL 0, SUCCESS 133885, TOTAL 133885\n"
]
}
],
"source": [
"from chainer_chemistry.dataset.preprocessors.ggnn_preprocessor import \\\n",
" GGNNPreprocessor\n",
" \n",
"preprocessor = GGNNPreprocessor()\n",
"dataset, dataset_smiles = datasets.get_qm9(preprocessor, labels=None, return_smiles=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check extracted dataset\n",
"\n",
"First, let's check type and number of dataset."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dataset information...\n",
"dataset