Note
Go to the end to download the full example code.
Basic usage of ANIDataset#
This supersedes the obsolete anidataloader. There are also builtin datasets that live in moria, and they can be directly downloaded through torchani.
# To begin with, let's import the modules we will use:
import shutil
from pathlib import Path
from torchani.datasets import ANIDataset
Downloading the builtin datasets performs a checksum to make sure the files are correct. If the function is called again and the dataset is already on the path, only the checksum is performed, the data is not downloaded. The output is an ANIDataset class Uncomment the following code to download (watch out, it may take some time):
# import torchani
# ds_1x = torchani.datasets.ANI1x('./datasets/ani1x/', download=True)
# ds_comp6 = torchani.datasets.COMP6v1('./datasets/comp6v1/', download=True)
# ds_2x = torchani.datasets.ANI2x('./datasets/ani2x/', download=True)
For the purposes of this example we will copy and modify two files inside torchani/dataset, which can be downloaded by running the download.sh script
file1_path = Path.cwd() / "file1.h5"
file2_path = Path.cwd() / "file2.h5"
shutil.copy(Path.cwd() / "../dataset/ani1-up_to_gdb4/ani_gdb_s01.h5", file1_path)
shutil.copy(Path.cwd() / "../dataset/ani1-up_to_gdb4/ani_gdb_s02.h5", file2_path)
PosixPath('/home/ipickering/Repos/ani/examples/file2.h5')
ANIDataset accepts a path to an h5 file or a list of paths to many files (optionally with names)
ds = ANIDataset(locations=(file1_path, file2_path), names=("file1", "file2"))
Verifying format correctness: 0it [00:00, ?it/s]
Verifying format correctness: 22it [00:00, 2586.83it/s]
/home/ipickering/Repos/ani/torchani/datasets/anidataset.py:351: UserWarning: {'energiesHE', 'smiles', 'coordinatesHE'} found in legacy dataset, this will generate unpredictable issues.
Probably .items() and .values() will work but not much else. It is highly recommended that you backup these properties (if needed) and *delete them* using dataset.delete_properties
warnings.warn(
Verifying format correctness: 0it [00:00, ?it/s]
Verifying format correctness: 92it [00:00, 2725.44it/s]
ANIDatasets have properties they can access. All conformers in the dataset have the same set of properties, lets check what properties this dataset holds
print(ds.properties)
{'energiesHE', 'energies', 'species', 'coordinates', 'smiles', 'coordinatesHE'}
When opening these files we see that we get a warning because they have some unsupported legacy properties, so the first thing we will do is delete them
ds.delete_properties(("coordinatesHE", "energiesHE", "smiles"))
print(ds.properties)
Deleting properties: 0%| | 0/3 [00:00<?, ?it/s]
Verifying format correctness: 0it [00:00, ?it/s]
Verifying format correctness: 13it [00:00, 4314.10it/s]
Deleting properties: 0%| | 0/13 [00:00<?, ?it/s]
Verifying format correctness: 0it [00:00, ?it/s]
Verifying format correctness: 53it [00:00, 4343.29it/s]
{'energies', 'species', 'coordinates'}
Conformer groups#
To access groups of conformers we can just use the dataset as an ordered dictionary
{'energies': tensor([-111.728, -111.746, -111.752, ..., -111.602, -111.791, -111.720],
dtype=torch.float64), 'species': tensor([[7, 7, 1, 1, 1, 1],
[7, 7, 1, 1, 1, 1],
[7, 7, 1, 1, 1, 1],
...,
[7, 7, 1, 1, 1, 1],
[7, 7, 1, 1, 1, 1],
[7, 7, 1, 1, 1, 1]]), 'coordinates': tensor([[[ 0.697, 0.092, 0.100],
[-0.702, 0.075, -0.092],
[ 1.195, -0.880, 0.527],
[ 1.080, 0.211, -0.925],
[-1.022, 0.201, 0.616],
[-1.187, -0.807, -0.350]],
[[ 0.699, 0.095, 0.083],
[-0.706, 0.073, -0.090],
[ 0.907, -0.947, 0.280],
[ 1.251, 0.194, -0.585],
[-1.218, 0.421, 0.924],
[-0.849, -0.980, -0.496]],
[[ 0.699, 0.060, 0.074],
[-0.717, 0.094, -0.102],
[ 1.058, -0.874, 0.373],
[ 1.230, 0.318, -0.487],
[-1.158, 0.331, 0.908],
[-0.868, -0.888, -0.390]],
...,
[[ 0.717, 0.075, 0.080],
[-0.707, 0.064, -0.119],
[ 0.914, -1.049, 0.358],
[ 1.189, 0.395, -0.495],
[-1.318, 0.402, 1.039],
[-0.907, -0.637, -0.353]],
[[ 0.700, 0.098, 0.092],
[-0.716, 0.059, -0.095],
[ 1.057, -0.761, 0.327],
[ 1.206, 0.264, -0.606],
[-1.129, 0.254, 0.811],
[-0.898, -0.905, -0.476]],
[[ 0.676, 0.118, 0.103],
[-0.690, 0.034, -0.093],
[ 1.337, -0.843, 0.466],
[ 1.128, 0.253, -0.906],
[-1.032, 0.308, 0.567],
[-1.224, -0.803, -0.315]]])}
We see that we get some tensors with properties, but this access is not very convenient, the keys seem to have weird mangled names which don’t say very much about what is in them.
print(list(ds.keys()))
['file1/gdb11_s01/gdb11_s01-0', 'file1/gdb11_s01/gdb11_s01-1', 'file1/gdb11_s01/gdb11_s01-2', 'file2/gdb11_s02/gdb11_s02-0', 'file2/gdb11_s02/gdb11_s02-1', 'file2/gdb11_s02/gdb11_s02-10', 'file2/gdb11_s02/gdb11_s02-11', 'file2/gdb11_s02/gdb11_s02-12', 'file2/gdb11_s02/gdb11_s02-2', 'file2/gdb11_s02/gdb11_s02-3', 'file2/gdb11_s02/gdb11_s02-4', 'file2/gdb11_s02/gdb11_s02-5', 'file2/gdb11_s02/gdb11_s02-6', 'file2/gdb11_s02/gdb11_s02-7', 'file2/gdb11_s02/gdb11_s02-8', 'file2/gdb11_s02/gdb11_s02-9']
This is because this dataset is in a legacy format, we can check that by querying the “grouping”
print(ds.grouping)
legacy
Before moving on, lets reformat this dataset so that it is in a more standarized format
ds.regroup_by_formula()
print(list(ds.keys()))
Regrouping by formulas: 0%| | 0/3 [00:00<?, ?it/s]
Regrouping by formulas: 0%| | 0/13 [00:00<?, ?it/s]
Regrouping by formulas: 15%|███████████████████▊ | 2/13 [00:00<00:00, 13.23it/s]
Regrouping by formulas: 46%|███████████████████████████████████████████████████████████▌ | 6/13 [00:00<00:00, 25.82it/s]
Regrouping by formulas: 85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 11/13 [00:00<00:00, 33.15it/s]
['file1/CH4', 'file1/H2O', 'file1/H3N', 'file2/C2H2', 'file2/C2H4', 'file2/C2H6', 'file2/CH2O', 'file2/CH4O', 'file2/CH5N', 'file2/H2N2', 'file2/H2O2', 'file2/H3NO', 'file2/H4N2', 'file2/HNO', 'file2/N2', 'file2/O2']
Now the dataset is organized by formulas, which makes access much easier (If we only had one file ds[‘CH4’] would have been enough)
group = ds["file1/CH4"]
items(), values() and keys() work as expected for groups of conformers, here we print only the first 100 as a sample
file1/CH4 {'energies': tensor([-40.481, -40.483, -40.485, ..., -40.496, -40.456, -40.465],
dtype=torch.float64), 'species': tensor([[6, 1, 1, 1, 1],
[6, 1, 1, 1, 1],
[6, 1, 1, 1, 1],
...,
[6, 1, 1, 1, 1],
[6, 1, 1, 1, 1],
[6, 1, 1, 1, 1]]), 'coordinates': tensor([[[-0.003, 0.010, 0.019],
[-0.795, 0.577, -0.547],
[-0.394, -0.980, 0.272],
[ 0.634, 0.447, 0.936],
[ 0.596, -0.165, -0.892]],
[[ 0.003, -0.020, 0.003],
[-0.783, 0.792, -0.260],
[-0.454, -1.030, 0.312],
[ 0.447, 0.636, 0.768],
[ 0.753, -0.159, -0.853]],
[[-0.018, -0.022, -0.011],
[-0.730, 0.682, -0.308],
[-0.382, -0.868, 0.382],
[ 0.569, 0.505, 0.804],
[ 0.755, -0.059, -0.744]],
...,
[[ 0.009, 0.002, -0.009],
[-0.855, 0.627, -0.261],
[-0.471, -0.946, 0.325],
[ 0.488, 0.509, 0.859],
[ 0.734, -0.216, -0.817]],
[[ 0.003, 0.017, 0.030],
[-0.764, 0.542, -0.671],
[-0.425, -1.045, 0.293],
[ 0.657, 0.410, 0.947],
[ 0.500, -0.106, -0.922]],
[[ 0.035, -0.002, -0.008],
[-0.954, 0.572, -0.159],
[-0.633, -0.924, 0.224],
[ 0.454, 0.590, 0.751],
[ 0.711, -0.216, -0.720]]])}
file1/H2O {'energies': tensor([-76.353, -76.362, -76.387, ..., -76.361, -76.374, -76.387],
dtype=torch.float64), 'species': tensor([[8, 1, 1],
[8, 1, 1],
[8, 1, 1],
...,
[8, 1, 1],
[8, 1, 1],
[8, 1, 1]]), 'coordinates': tensor([[[ -0.000, -0.006, 0.107],
[ 0.000, 0.777, -0.421],
[ 0.000, -0.678, -0.343]],
[[ 0.000, -0.005, 0.133],
[ -0.000, 0.639, -0.614],
[ 0.000, -0.566, -0.557]],
[[ 0.000, 0.001, 0.123],
[ -0.000, 0.718, -0.504],
[ -0.000, -0.727, -0.511]],
...,
[[ 0.000, 0.005, 0.124],
[ -0.000, 0.589, -0.487],
[ -0.000, -0.667, -0.548]],
[[ 0.000, 0.007, 0.131],
[ -0.000, 0.666, -0.528],
[ -0.000, -0.771, -0.610]],
[[ 0.000, 0.001, 0.122],
[ -0.000, 0.743, -0.495],
[ -0.000, -0.764, -0.512]]])}
file1/H3N {'energies': tensor([-56.510, -56.502, -56.507, ..., -56.508, -56.518, -56.521],
dtype=torch.float64), 'species': tensor([[7, 1, 1, 1],
[7, 1, 1, 1],
[7, 1, 1, 1],
...,
[7, 1, 1, 1],
[7, 1, 1, 1],
[7, 1, 1, 1]]), 'coordinates': tensor([[[ 0.020, 0.006, -0.078],
[ 0.385, -0.882, 0.067],
[ 0.318, 0.931, 0.038],
[-0.979, -0.132, 0.169]],
[[ 0.003, -0.015, -0.143],
[ 0.533, -0.736, 0.341],
[ 0.229, 0.820, 0.439],
[-0.803, 0.118, 0.400]],
[[-0.007, 0.010, -0.095],
[ 0.566, -0.902, 0.221],
[ 0.528, 0.938, 0.149],
[-0.991, -0.180, 0.147]],
...,
[[-0.019, 0.005, -0.134],
[ 0.636, -0.745, 0.409],
[ 0.556, 0.791, 0.365],
[-0.922, -0.118, 0.278]],
[[-0.003, 0.007, -0.135],
[ 0.450, -0.787, 0.383],
[ 0.475, 0.874, 0.336],
[-0.889, -0.184, 0.347]],
[[ 0.003, -0.003, -0.135],
[ 0.538, -0.818, 0.344],
[ 0.344, 0.872, 0.365],
[-0.919, -0.014, 0.368]]])}
file2/C2H2 {'energies': tensor([-77.281, -77.286, -77.283, ..., -77.295, -77.286, -77.294],
dtype=torch.float64), 'species': tensor([[6, 6, 1, 1],
[6, 6, 1, 1],
[6, 6, 1, 1],
...,
[6, 6, 1, 1],
[6, 6, 1, 1],
[6, 6, 1, 1]]), 'coordinates': tensor([[[ 0.001, 0.026, 0.628],
[ -0.017, -0.055, -0.625],
[ 0.058, -0.004, 1.644],
[ 0.134, 0.347, -1.687]],
[[ 0.014, -0.043, 0.589],
[ -0.043, 0.034, -0.586],
[ 0.049, 0.220, 1.604],
[ 0.293, -0.113, -1.640]],
[[ -0.043, -0.046, 0.615],
[ 0.056, 0.031, -0.622],
[ 0.134, 0.256, 1.711],
[ -0.290, -0.078, -1.639]],
...,
[[ 0.029, -0.004, 0.608],
[ -0.024, -0.007, -0.605],
[ -0.147, 0.057, 1.645],
[ 0.081, 0.070, -1.677]],
[[ 0.021, -0.052, 0.617],
[ -0.041, 0.039, -0.614],
[ -0.017, 0.275, 1.658],
[ 0.253, -0.116, -1.696]],
[[ 0.002, 0.011, 0.609],
[ 0.004, -0.010, -0.611],
[ -0.031, -0.056, 1.658],
[ -0.039, 0.036, -1.632]]])}
file2/C2H4 {'energies': tensor([-78.555, -78.554, -78.528, ..., -78.548, -78.544, -78.533],
dtype=torch.float64), 'species': tensor([[6, 6, 1, 1, 1, 1],
[6, 6, 1, 1, 1, 1],
[6, 6, 1, 1, 1, 1],
...,
[6, 6, 1, 1, 1, 1],
[6, 6, 1, 1, 1, 1],
[6, 6, 1, 1, 1, 1]]), 'coordinates': tensor([[[-0.657, 0.071, -0.040],
[ 0.653, -0.061, 0.045],
[-1.287, -0.857, 0.017],
[-1.173, 1.006, -0.253],
[ 1.136, -1.000, 0.200],
[ 1.365, 0.736, -0.031]],
[[-0.667, 0.069, -0.045],
[ 0.664, -0.072, 0.038],
[-1.330, -0.812, 0.087],
[-1.113, 1.049, -0.251],
[ 1.227, -1.012, 0.223],
[ 1.253, 0.812, 0.042]],
[[-0.620, 0.050, -0.014],
[ 0.603, -0.043, 0.025],
[-1.254, -0.800, -0.028],
[-1.109, 1.022, -0.362],
[ 1.122, -1.068, 0.214],
[ 1.445, 0.778, 0.043]],
...,
[[-0.663, 0.078, -0.014],
[ 0.677, -0.068, 0.036],
[-1.306, -0.848, -0.067],
[-1.212, 0.950, -0.393],
[ 1.062, -1.026, 0.174],
[ 1.307, 0.793, -0.005]],
[[-0.661, 0.064, -0.070],
[ 0.642, -0.051, 0.070],
[-1.233, -0.805, 0.138],
[-1.218, 1.025, -0.177],
[ 1.199, -1.082, 0.111],
[ 1.468, 0.723, -0.083]],
[[-0.653, 0.052, -0.009],
[ 0.671, -0.063, 0.042],
[-1.524, -0.691, -0.098],
[-1.163, 0.924, -0.422],
[ 1.144, -1.035, 0.132],
[ 1.373, 0.911, -0.027]]])}
file2/C2H6 {'energies': tensor([-79.764, -79.783, -79.655, ..., -79.781, -79.745, -79.745],
dtype=torch.float64), 'species': tensor([[6, 6, 1, ..., 1, 1, 1],
[6, 6, 1, ..., 1, 1, 1],
[6, 6, 1, ..., 1, 1, 1],
...,
[6, 6, 1, ..., 1, 1, 1],
[6, 6, 1, ..., 1, 1, 1],
[6, 6, 1, ..., 1, 1, 1]]), 'coordinates': tensor([[[ 0.759, -0.034, 0.022],
[ -0.740, 0.007, -0.027],
[ 1.183, 0.953, -0.303],
...,
[ -1.167, 0.760, 0.679],
[ -1.126, -0.958, 0.512],
[ -1.129, 0.260, -0.968]],
[[ 0.760, -0.009, -0.005],
[ -0.757, 0.005, 0.003],
[ 1.226, 0.934, -0.248],
...,
[ -1.145, 0.696, 0.668],
[ -1.174, -1.024, 0.384],
[ -1.148, 0.345, -1.018]],
[[ 0.749, -0.019, -0.013],
[ -0.770, 0.001, -0.003],
[ 1.387, 1.111, -0.434],
...,
[ -1.185, 0.910, 0.923],
[ -1.098, -0.926, 0.125],
[ -1.177, 0.091, -0.983]],
...,
[[ 0.771, -0.007, -0.021],
[ -0.777, -0.001, 0.005],
[ 1.191, 0.959, -0.270],
...,
[ -1.123, 0.736, 0.779],
[ -1.168, -0.916, 0.247],
[ -1.126, 0.235, -0.988]],
[[ 0.774, 0.000, -0.017],
[ -0.773, 0.002, 0.008],
[ 1.184, 0.918, -0.116],
...,
[ -1.129, 0.625, 0.631],
[ -1.219, -1.021, 0.420],
[ -1.159, 0.411, -1.041]],
[[ 0.782, 0.002, 0.027],
[ -0.792, -0.004, -0.014],
[ 1.118, 0.943, -0.228],
...,
[ -1.246, 0.707, 0.636],
[ -1.167, -1.070, 0.509],
[ -1.014, 0.365, -1.068]]])}
file2/CH2O {'energies': tensor([-114.457, -114.452, -114.453, ..., -114.452, -114.452, -114.450],
dtype=torch.float64), 'species': tensor([[6, 8, 1, 1],
[6, 8, 1, 1],
[6, 8, 1, 1],
...,
[6, 8, 1, 1],
[6, 8, 1, 1],
[6, 8, 1, 1]]), 'coordinates': tensor([[[ -0.515, 0.011, 0.019],
[ 0.666, -0.003, -0.005],
[ -1.117, 0.960, -0.079],
[ -1.121, -1.050, -0.079]],
[[ -0.510, 0.011, 0.020],
[ 0.648, -0.009, -0.005],
[ -1.089, 0.981, -0.083],
[ -0.908, -0.973, -0.083]],
[[ -0.530, -0.025, -0.019],
[ 0.671, 0.010, 0.004],
[ -1.006, 1.103, 0.077],
[ -1.132, -0.970, 0.077]],
...,
[[ -0.515, -0.012, 0.028],
[ 0.669, 0.011, -0.006],
[ -1.024, 0.976, -0.113],
[ -1.263, -1.005, -0.113]],
[[ -0.540, 0.018, -0.005],
[ 0.693, -0.014, 0.001],
[ -1.336, 0.973, 0.020],
[ -1.040, -0.958, 0.020]],
[[ -0.548, -0.006, -0.036],
[ 0.689, 0.008, 0.008],
[ -1.008, 0.969, 0.147],
[ -1.204, -1.027, 0.147]]])}
file2/CH4O {'energies': tensor([-115.604, -115.672, -115.631, ..., -115.627, -115.663, -115.598],
dtype=torch.float64), 'species': tensor([[6, 8, 1, 1, 1, 1],
[6, 8, 1, 1, 1, 1],
[6, 8, 1, 1, 1, 1],
...,
[6, 8, 1, 1, 1, 1],
[6, 8, 1, 1, 1, 1],
[6, 8, 1, 1, 1, 1]]), 'coordinates': tensor([[[ 0.631, -0.033, 0.018],
[-0.718, 0.126, -0.050],
[ 1.036, 1.003, 0.231],
[ 0.961, -0.630, -0.938],
[ 1.143, -0.393, 0.733],
[-1.214, -0.767, 0.579]],
[[ 0.679, -0.010, -0.006],
[-0.756, 0.114, 0.016],
[ 0.974, 0.960, -0.100],
[ 1.119, -0.522, -0.861],
[ 1.011, -0.534, 0.941],
[-1.150, -0.737, -0.156]],
[[ 0.601, 0.003, -0.027],
[-0.706, 0.113, -0.022],
[ 0.977, 0.957, 0.172],
[ 1.227, -0.743, -0.826],
[ 0.892, -0.460, 0.829],
[-1.017, -0.754, 0.498]],
...,
[[ 0.691, -0.044, -0.005],
[-0.777, 0.133, -0.010],
[ 1.264, 1.143, 0.013],
[ 1.214, -0.658, -0.912],
[ 0.929, -0.461, 0.853],
[-1.278, -0.772, 0.264]],
[[ 0.678, -0.015, -0.030],
[-0.768, 0.118, -0.009],
[ 1.065, 0.936, 0.192],
[ 1.207, -0.530, -0.839],
[ 0.936, -0.475, 0.841],
[-1.075, -0.781, 0.297]],
[[ 0.623, -0.042, -0.027],
[-0.689, 0.139, -0.025],
[ 0.987, 0.969, 0.247],
[ 1.040, -0.608, -0.971],
[ 0.715, -0.399, 0.911],
[-1.192, -0.829, 0.545]]])}
file2/CH5N {'energies': tensor([-95.805, -95.715, -95.634, ..., -95.776, -95.779, -95.716],
dtype=torch.float64), 'species': tensor([[6, 7, 1, ..., 1, 1, 1],
[6, 7, 1, ..., 1, 1, 1],
[6, 7, 1, ..., 1, 1, 1],
...,
[6, 7, 1, ..., 1, 1, 1],
[6, 7, 1, ..., 1, 1, 1],
[6, 7, 1, ..., 1, 1, 1]]), 'coordinates': tensor([[[ 0.712, 0.008, 0.032],
[ -0.749, -0.008, -0.127],
[ 1.198, -0.838, -0.455],
...,
[ 0.904, -0.100, 1.049],
[ -1.221, -0.758, 0.196],
[ -1.020, 0.826, 0.414]],
[[ 0.723, -0.003, 0.012],
[ -0.766, -0.023, -0.146],
[ 1.298, -0.812, -0.347],
...,
[ 1.169, -0.146, 1.182],
[ -1.361, -0.687, 0.231],
[ -1.048, 1.000, 0.730]],
[[ 0.687, -0.021, -0.008],
[ -0.715, -0.022, -0.127],
[ 1.009, -0.765, -0.273],
...,
[ 1.199, -0.069, 1.138],
[ -1.243, -0.613, 0.164],
[ -1.099, 0.925, 0.678]],
...,
[[ 0.673, -0.008, 0.009],
[ -0.717, -0.005, -0.119],
[ 0.886, -0.858, -0.439],
...,
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file1/CH4
file1/H2O
file1/H3N
file2/C2H2
file2/C2H4
file2/C2H6
file2/CH2O
file2/CH4O
file2/CH5N
file2/H2N2
file2/H2O2
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[-0.714, -0.083, -0.048],
[ 0.884, -0.779, 0.251],
[-0.869, 0.587, 0.262]]])}
To get the number of groups of conformers we can use len(), or also dataset.num_conformer_groups
num_groups = len(ds)
print(num_groups)
16
To get the number of conformers we can use num_conformers
num_conformers = ds.num_conformers
print(num_conformers)
61762
Conformers#
To access individual conformers or subsets of conformers we use “conformer” methods, get_conformers and iter_conformers
{'energies': tensor(-40.481, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.003, 0.010, 0.019],
[-0.795, 0.577, -0.547],
[-0.394, -0.980, 0.272],
[ 0.634, 0.447, 0.936],
[ 0.596, -0.165, -0.892]])}
{'energies': tensor(-40.483, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.003, -0.020, 0.003],
[-0.783, 0.792, -0.260],
[-0.454, -1.030, 0.312],
[ 0.447, 0.636, 0.768],
[ 0.753, -0.159, -0.853]])}
A tensor / list / array can also be passed for indexing, to fetch multiple conformers from the same group, which is faster. Since we copy the data forh simplicity, this allows all fancy indexing operations (directly indexing using h5py for example does not).
conformers = ds.get_conformers("file1/CH4", [0, 1])
print(conformers)
{'energies': tensor([-40.481, -40.483], dtype=torch.float64), 'species': tensor([[6, 1, 1, 1, 1],
[6, 1, 1, 1, 1]]), 'coordinates': tensor([[[-0.003, 0.010, 0.019],
[-0.795, 0.577, -0.547],
[-0.394, -0.980, 0.272],
[ 0.634, 0.447, 0.936],
[ 0.596, -0.165, -0.892]],
[[ 0.003, -0.020, 0.003],
[-0.783, 0.792, -0.260],
[-0.454, -1.030, 0.312],
[ 0.447, 0.636, 0.768],
[ 0.753, -0.159, -0.853]]])}
We can also access all the group if we don’t pass an index, same as normal indexing
{'energies': tensor([-40.481, -40.483, -40.485, ..., -40.496, -40.456, -40.465],
dtype=torch.float64), 'species': tensor([[6, 1, 1, 1, 1],
[6, 1, 1, 1, 1],
[6, 1, 1, 1, 1],
...,
[6, 1, 1, 1, 1],
[6, 1, 1, 1, 1],
[6, 1, 1, 1, 1]]), 'coordinates': tensor([[[-0.003, 0.010, 0.019],
[-0.795, 0.577, -0.547],
[-0.394, -0.980, 0.272],
[ 0.634, 0.447, 0.936],
[ 0.596, -0.165, -0.892]],
[[ 0.003, -0.020, 0.003],
[-0.783, 0.792, -0.260],
[-0.454, -1.030, 0.312],
[ 0.447, 0.636, 0.768],
[ 0.753, -0.159, -0.853]],
[[-0.018, -0.022, -0.011],
[-0.730, 0.682, -0.308],
[-0.382, -0.868, 0.382],
[ 0.569, 0.505, 0.804],
[ 0.755, -0.059, -0.744]],
...,
[[ 0.009, 0.002, -0.009],
[-0.855, 0.627, -0.261],
[-0.471, -0.946, 0.325],
[ 0.488, 0.509, 0.859],
[ 0.734, -0.216, -0.817]],
[[ 0.003, 0.017, 0.030],
[-0.764, 0.542, -0.671],
[-0.425, -1.045, 0.293],
[ 0.657, 0.410, 0.947],
[ 0.500, -0.106, -0.922]],
[[ 0.035, -0.002, -0.008],
[-0.954, 0.572, -0.159],
[-0.633, -0.924, 0.224],
[ 0.454, 0.590, 0.751],
[ 0.711, -0.216, -0.720]]])}
Finally, it is possible to also specify which properties we want using ‘properties’
{'energies': tensor([-40.481, -40.492], dtype=torch.float64), 'species': tensor([[6, 1, 1, 1, 1],
[6, 1, 1, 1, 1]])}
If you want you can also get the conformers as numpy arrays by calling get_numpy_conformers. this has an optional flag “chem_symbols” which if specified “True” will output the elements as strings (‘C’, ‘H’, ‘H’, … etc)
{'energies': array([-40.48058817, -40.48311923]), 'species': array([['C', 'H', 'H', 'H', 'H'],
['C', 'H', 'H', 'H', 'H']], dtype='<U1'), 'coordinates': array([[[-0.0034502 , 0.01017081, 0.01938033],
[-0.7954868 , 0.5766599 , -0.5472012 ],
[-0.39378393, -0.97992676, 0.2722862 ],
[ 0.6344988 , 0.4473651 , 0.93568736],
[ 0.59581804, -0.16517928, -0.8915708 ]],
[[ 0.00311385, -0.02007288, 0.00282224],
[-0.78331304, 0.7921426 , -0.26027855],
[-0.45410746, -1.0295471 , 0.31240797],
[ 0.44713658, 0.63571125, 0.76770777],
[ 0.7531731 , -0.1592813 , -0.85348135]]], dtype=float32)}
We can iterate over all conformers sequentially by calling iter_conformer, (this is faster than doing it manually since it caches each conformer group previous to starting the iteration), here we print the first 100 as a sample
{'energies': tensor(-40.481, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.003, 0.010, 0.019],
[-0.795, 0.577, -0.547],
[-0.394, -0.980, 0.272],
[ 0.634, 0.447, 0.936],
[ 0.596, -0.165, -0.892]])}
{'energies': tensor(-40.483, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.003, -0.020, 0.003],
[-0.783, 0.792, -0.260],
[-0.454, -1.030, 0.312],
[ 0.447, 0.636, 0.768],
[ 0.753, -0.159, -0.853]])}
{'energies': tensor(-40.485, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.018, -0.022, -0.011],
[-0.730, 0.682, -0.308],
[-0.382, -0.868, 0.382],
[ 0.569, 0.505, 0.804],
[ 0.755, -0.059, -0.744]])}
{'energies': tensor(-40.492, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.007, 0.013, 0.015],
[-0.828, 0.624, -0.399],
[-0.413, -1.013, 0.289],
[ 0.574, 0.438, 0.783],
[ 0.754, -0.207, -0.848]])}
{'energies': tensor(-40.456, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.007, 0.035, 0.028],
[-1.012, 0.457, -0.554],
[-0.519, -0.962, 0.271],
[ 0.698, 0.285, 0.818],
[ 0.749, -0.196, -0.868]])}
{'energies': tensor(-40.489, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.010, -0.012, 0.011],
[-0.788, 0.640, -0.412],
[-0.475, -0.980, 0.279],
[ 0.656, 0.565, 0.809],
[ 0.726, -0.079, -0.805]])}
{'energies': tensor(-40.488, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.006, -0.011, -0.013],
[-0.731, 0.733, -0.327],
[-0.307, -0.918, 0.387],
[ 0.459, 0.476, 0.874],
[ 0.648, -0.160, -0.784]])}
{'energies': tensor(-40.497, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.003, 0.004, -0.004],
[-0.828, 0.666, -0.301],
[-0.377, -0.942, 0.322],
[ 0.501, 0.455, 0.816],
[ 0.734, -0.224, -0.786]])}
{'energies': tensor(-40.479, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.009, 0.009, -0.019],
[-0.728, 0.646, -0.245],
[-0.289, -0.919, 0.351],
[ 0.447, 0.431, 0.867],
[ 0.673, -0.264, -0.744]])}
{'energies': tensor(-40.498, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ -0.007, -0.000, -0.002],
[ -0.817, 0.676, -0.322],
[ -0.381, -0.955, 0.314],
[ 0.537, 0.487, 0.830],
[ 0.742, -0.205, -0.800]])}
{'energies': tensor(-40.491, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.007, 0.000, 0.008],
[ -0.754, 0.594, -0.402],
[ -0.461, -0.930, 0.376],
[ 0.472, 0.443, 0.852],
[ 0.665, -0.110, -0.927]])}
{'energies': tensor(-40.465, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.023, 0.010, -0.011],
[-0.944, 0.498, -0.285],
[-0.569, -0.812, 0.429],
[ 0.463, 0.318, 0.791],
[ 0.772, -0.119, -0.807]])}
{'energies': tensor(-40.452, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.021, 0.015, -0.014],
[-0.753, 0.591, -0.193],
[-0.397, -1.036, 0.242],
[ 0.623, 0.506, 0.684],
[ 0.781, -0.235, -0.569]])}
{'energies': tensor(-40.481, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.011, -0.028, -0.016],
[-0.733, 0.780, -0.205],
[-0.393, -0.955, 0.329],
[ 0.500, 0.659, 0.838],
[ 0.760, -0.149, -0.774]])}
{'energies': tensor(-40.484, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.010, 0.013, -0.012],
[-0.928, 0.628, -0.352],
[-0.370, -0.867, 0.397],
[ 0.474, 0.348, 0.957],
[ 0.710, -0.258, -0.856]])}
{'energies': tensor(-40.496, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.002, -0.011, 0.001],
[-0.752, 0.659, -0.377],
[-0.448, -0.946, 0.374],
[ 0.505, 0.492, 0.817],
[ 0.667, -0.075, -0.831]])}
{'energies': tensor(-40.472, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.033, -0.008, -0.018],
[-0.818, 0.590, -0.315],
[-0.534, -0.821, 0.527],
[ 0.330, 0.378, 0.906],
[ 0.627, -0.049, -0.904]])}
{'energies': tensor(-40.458, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.005, -0.008, -0.010],
[-0.738, 0.838, -0.357],
[-0.189, -0.964, 0.352],
[ 0.415, 0.458, 0.849],
[ 0.574, -0.233, -0.729]])}
{'energies': tensor(-40.484, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.018, -0.003, -0.004],
[-0.759, 0.817, -0.357],
[-0.251, -1.011, 0.409],
[ 0.478, 0.425, 0.798],
[ 0.742, -0.194, -0.798]])}
{'energies': tensor(-40.452, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.012, -0.034, 0.015],
[-0.687, 0.752, -0.402],
[-0.472, -0.930, 0.471],
[ 0.496, 0.477, 0.636],
[ 0.807, 0.104, -0.886]])}
{'energies': tensor(-40.462, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.044, -0.007, -0.033],
[-0.781, 0.766, -0.347],
[-0.228, -0.925, 0.542],
[ 0.650, 0.368, 0.949],
[ 0.879, -0.127, -0.753]])}
{'energies': tensor(-40.480, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.023, -0.015, 0.004],
[-0.686, 0.702, -0.419],
[-0.406, -0.990, 0.449],
[ 0.581, 0.483, 0.825],
[ 0.783, -0.018, -0.899]])}
{'energies': tensor(-40.489, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.003, 0.002, 0.023],
[-0.745, 0.626, -0.414],
[-0.379, -0.947, 0.234],
[ 0.517, 0.477, 0.754],
[ 0.644, -0.176, -0.841]])}
{'energies': tensor(-40.473, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.002, 0.024, -0.015],
[-0.825, 0.526, -0.225],
[-0.462, -0.978, 0.329],
[ 0.522, 0.470, 0.928],
[ 0.788, -0.298, -0.855]])}
{'energies': tensor(-40.492, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.010, -0.014, -0.004],
[-0.818, 0.746, -0.280],
[-0.374, -0.938, 0.312],
[ 0.526, 0.540, 0.788],
[ 0.781, -0.181, -0.777]])}
{'energies': tensor(-40.489, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.002, 0.014, 0.004],
[-0.821, 0.646, -0.290],
[-0.415, -1.055, 0.232],
[ 0.528, 0.543, 0.829],
[ 0.728, -0.300, -0.819]])}
{'energies': tensor(-40.453, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.017, -0.006, -0.034],
[-0.691, 0.708, -0.166],
[-0.373, -0.949, 0.426],
[ 0.294, 0.524, 0.910],
[ 0.571, -0.210, -0.761]])}
{'energies': tensor(-40.468, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.029, -0.014, 0.035],
[-0.875, 0.561, -0.464],
[-0.652, -0.928, 0.219],
[ 0.535, 0.598, 0.819],
[ 0.642, -0.066, -0.994]])}
{'energies': tensor(-40.464, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.007, -0.024, -0.010],
[-0.667, 0.865, -0.327],
[-0.316, -1.064, 0.382],
[ 0.440, 0.656, 0.955],
[ 0.631, -0.166, -0.886]])}
{'energies': tensor(-40.479, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.024, -0.009, 0.007],
[-0.917, 0.537, -0.344],
[-0.563, -0.782, 0.313],
[ 0.490, 0.449, 0.812],
[ 0.704, -0.102, -0.859]])}
{'energies': tensor(-40.495, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.001, -0.005, 0.003],
[ -0.812, 0.604, -0.376],
[ -0.462, -0.917, 0.309],
[ 0.575, 0.483, 0.804],
[ 0.689, -0.108, -0.778]])}
{'energies': tensor(-40.491, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.008, 0.010, 0.013],
[-0.743, 0.659, -0.416],
[-0.320, -1.019, 0.248],
[ 0.537, 0.469, 0.808],
[ 0.619, -0.232, -0.795]])}
{'energies': tensor(-40.460, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.019, 0.001, -0.036],
[-0.764, 0.663, -0.319],
[-0.389, -0.903, 0.590],
[ 0.339, 0.340, 0.998],
[ 0.585, -0.116, -0.838]])}
{'energies': tensor(-40.494, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.011, 0.004, 0.004],
[-0.788, 0.630, -0.317],
[-0.463, -0.981, 0.323],
[ 0.441, 0.508, 0.849],
[ 0.684, -0.201, -0.901]])}
{'energies': tensor(-40.460, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.035, -0.020, -0.004],
[-0.741, 0.608, -0.192],
[-0.605, -0.947, 0.237],
[ 0.367, 0.730, 0.869],
[ 0.564, -0.155, -0.861]])}
{'energies': tensor(-40.494, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.000, 0.003, 0.011],
[ -0.852, 0.660, -0.378],
[ -0.395, -0.968, 0.231],
[ 0.560, 0.499, 0.800],
[ 0.683, -0.223, -0.782]])}
{'energies': tensor(-40.490, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.001, -0.008, 0.002],
[-0.792, 0.710, -0.395],
[-0.409, -1.003, 0.321],
[ 0.534, 0.554, 0.910],
[ 0.654, -0.165, -0.864]])}
{'energies': tensor(-40.470, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.005, -0.021, -0.001],
[-0.661, 0.607, -0.302],
[-0.501, -0.897, 0.381],
[ 0.508, 0.526, 0.719],
[ 0.708, 0.018, -0.782]])}
{'energies': tensor(-40.497, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.003, 0.004, -0.003],
[-0.813, 0.689, -0.321],
[-0.350, -0.956, 0.330],
[ 0.486, 0.455, 0.837],
[ 0.709, -0.232, -0.805]])}
{'energies': tensor(-40.498, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.001, 0.002, -0.005],
[-0.828, 0.663, -0.320],
[-0.398, -0.945, 0.353],
[ 0.502, 0.455, 0.848],
[ 0.736, -0.200, -0.824]])}
{'energies': tensor(-40.471, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.006, -0.013, 0.006],
[-0.760, 0.628, -0.404],
[-0.570, -0.985, 0.447],
[ 0.529, 0.457, 0.712],
[ 0.724, 0.053, -0.833]])}
{'energies': tensor(-40.494, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.003, -0.000, -0.014],
[ -0.856, 0.668, -0.352],
[ -0.398, -0.922, 0.410],
[ 0.516, 0.417, 0.895],
[ 0.698, -0.160, -0.787]])}
{'energies': tensor(-40.495, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.010, -0.000, -0.004],
[ -0.828, 0.591, -0.349],
[ -0.500, -0.946, 0.346],
[ 0.530, 0.499, 0.895],
[ 0.681, -0.144, -0.847]])}
{'energies': tensor(-40.468, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.025, 0.010, 0.018],
[-0.864, 0.565, -0.491],
[-0.406, -0.989, 0.193],
[ 0.806, 0.489, 0.854],
[ 0.762, -0.186, -0.775]])}
{'energies': tensor(-40.459, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.011, 0.030, 0.034],
[-0.835, 0.530, -0.524],
[-0.369, -0.970, 0.177],
[ 0.506, 0.431, 0.989],
[ 0.562, -0.351, -1.042]])}
{'energies': tensor(-40.455, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.027, -0.032, -0.021],
[-0.623, 0.815, -0.305],
[-0.416, -1.043, 0.573],
[ 0.524, 0.562, 0.844],
[ 0.831, 0.043, -0.867]])}
{'energies': tensor(-40.486, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.019, 0.020, -0.007],
[-0.796, 0.600, -0.372],
[-0.292, -0.964, 0.306],
[ 0.612, 0.393, 0.896],
[ 0.700, -0.268, -0.741]])}
{'energies': tensor(-40.481, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.008, 0.021, -0.015],
[-0.896, 0.692, -0.265],
[-0.291, -0.970, 0.345],
[ 0.492, 0.397, 0.909],
[ 0.794, -0.367, -0.808]])}
{'energies': tensor(-40.474, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.013, 0.016, 0.006],
[-0.839, 0.612, -0.504],
[-0.393, -0.980, 0.464],
[ 0.620, 0.260, 0.789],
[ 0.771, -0.080, -0.819]])}
{'energies': tensor(-40.485, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.007, 0.032, -0.005],
[-0.869, 0.537, -0.327],
[-0.431, -0.981, 0.349],
[ 0.499, 0.331, 0.823],
[ 0.713, -0.264, -0.788]])}
{'energies': tensor(-40.456, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.007, -0.023, 0.011],
[-0.745, 0.668, -0.201],
[-0.606, -1.059, 0.157],
[ 0.538, 0.775, 0.652],
[ 0.728, -0.114, -0.744]])}
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{'energies': tensor(-40.488, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.013, -0.022, -0.005],
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{'energies': tensor(-40.487, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.001, 0.018, 0.013],
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{'energies': tensor(-40.454, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.041, -0.003, 0.001],
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{'energies': tensor(-40.495, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ -0.000, 0.003, -0.016],
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{'energies': tensor(-40.481, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.014, -0.012, 0.000],
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{'energies': tensor(-40.487, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.002, 0.007, -0.023],
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{'energies': tensor(-40.493, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.006, -0.001, 0.002],
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{'energies': tensor(-40.459, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.038, 0.005, -0.030],
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{'energies': tensor(-40.451, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.042, 0.013, -0.008],
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{'energies': tensor(-40.480, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.002, -0.018, 0.013],
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{'energies': tensor(-40.475, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.023, -0.009, -0.015],
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{'energies': tensor(-40.464, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.017, -0.003, -0.011],
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{'energies': tensor(-40.461, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.011, -0.019, 0.032],
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{'energies': tensor(-40.460, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.035, -0.008, 0.000],
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{'energies': tensor(-40.499, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.001, -0.002, 0.004],
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{'energies': tensor(-40.486, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.020, -0.013, 0.010],
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{'energies': tensor(-40.497, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.008, -0.005, -0.008],
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{'energies': tensor(-40.489, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ -0.010, -0.000, 0.005],
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{'energies': tensor(-40.493, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.014, 0.006, -0.006],
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{'energies': tensor(-40.466, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.010, -0.021, 0.011],
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{'energies': tensor(-40.494, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.003, 0.002, 0.018],
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{'energies': tensor(-40.466, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.014, 0.021, -0.031],
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{'energies': tensor(-40.491, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ -0.018, 0.011, -0.001],
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{'energies': tensor(-40.465, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ -0.033, -0.008, 0.000],
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{'energies': tensor(-40.492, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.000, 0.006, -0.004],
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{'energies': tensor(-40.498, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.002, 0.003, -0.003],
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{'energies': tensor(-40.464, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.049, 0.011, 0.006],
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{'energies': tensor(-40.498, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.002, -0.009, 0.006],
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{'energies': tensor(-40.472, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ -0.028, -0.000, 0.026],
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{'energies': tensor(-40.477, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.009, 0.002, 0.012],
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{'energies': tensor(-40.494, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.014, 0.007, -0.004],
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{'energies': tensor(-40.468, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.022, -0.016, 0.014],
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{'energies': tensor(-40.487, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.015, -0.001, 0.004],
[ -0.842, 0.545, -0.340],
[ -0.586, -0.966, 0.285],
[ 0.567, 0.545, 0.822],
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{'energies': tensor(-40.477, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.018, -0.005, -0.030],
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{'energies': tensor(-40.459, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.004, -0.032, -0.004],
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{'energies': tensor(-40.480, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.030, 0.006, -0.020],
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{'energies': tensor(-40.465, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.005, -0.009, 0.026],
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[ 0.594, 0.527, 0.789],
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{'energies': tensor(-40.471, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.022, 0.012, -0.015],
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[ 0.544, 0.425, 0.749],
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{'energies': tensor(-40.463, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.015, -0.014, -0.018],
[-0.851, 0.659, -0.343],
[-0.397, -0.738, 0.520],
[ 0.381, 0.319, 0.892],
[ 0.688, -0.075, -0.861]])}
{'energies': tensor(-40.491, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.014, -0.003, -0.018],
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[ 0.499, 0.484, 0.893],
[ 0.700, -0.133, -0.792]])}
{'energies': tensor(-40.488, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.008, 0.021, 0.011],
[-0.857, 0.564, -0.374],
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[ 0.595, 0.439, 0.865],
[ 0.731, -0.306, -0.837]])}
{'energies': tensor(-40.468, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.000, 0.024, 0.002],
[ -0.870, 0.541, -0.504],
[ -0.367, -0.960, 0.298],
[ 0.631, 0.396, 1.054],
[ 0.600, -0.263, -0.873]])}
{'energies': tensor(-40.478, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[-0.015, -0.020, 0.001],
[-0.687, 0.684, -0.421],
[-0.412, -0.939, 0.422],
[ 0.585, 0.454, 0.722],
[ 0.697, 0.045, -0.738]])}
{'energies': tensor(-40.491, dtype=torch.float64), 'species': tensor([6, 1, 1, 1, 1]), 'coordinates': tensor([[ 0.005, -0.002, -0.020],
[-0.809, 0.667, -0.308],
[-0.444, -0.966, 0.452],
[ 0.486, 0.465, 0.925],
[ 0.711, -0.140, -0.828]])}
We will now delete the files we copied for cleanup purposes