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"# Example 1: GAP 2017 Carbon \n",
"## Tasks\n",
" - Train energy, forces, and virials of [carbon](https://doi.org/10.1103/PhysRevB.95.094203).\n",
" - Plot the properties calculated by HotPP vs DFT.\n",
" - Use ASE Calculator interface.\n",
" - Plot the phono spectrum. \n",
"\n",
"## Input \n",
"You can see the following files required by this task in this fold.\n",
"\n",
"```bash\n",
"carbon\n",
"|- input.yaml \n",
"|- model.ckpt\n",
"|- data/\n",
"| |- train.traj \n",
"| |- test.traj\n",
"```\n",
"- `model.ckpt` is a checkpoint of pre-trained model, we will continue our train from this to quickly reproduce the results.\n",
"- `train.traj` is the training data, it can be any format which can be red by `ase` if the `type` is `ase`. The target energy should be stored in `info['energy']` or can be got by `get_potential_energy()`, the target forces should be stored in `info['forces']` or can be got by `get_forces()`, the target virials should be stored in `info['virial']` (or `info['stress']`) or can be got by `get_stress()` , we will convert it to `virial` by multiple `-volume`.\n",
"- `test.traj` is the testset.\n",
"- [`input.yaml`](https://gitlab.com/bigd4/hotpp/-/blob/master/examples/carbon/input.yaml) controls the specific details of model architecture and training. Some import parameters in this tasks are:\n",
"\n",
"```yaml\n",
"cutoff: 3.0\n",
"Data:\n",
" type: ase\n",
" trainSet: data/train.traj \n",
" testSet: data/test.traj\n",
"Train:\n",
" allowMissing: True\n",
" targetProp: [\"energy\", \"forces\", \"virial\"] \n",
" weight: [0.5, 1.0, 0.2]\n",
" maxEpoch: 10\n",
"```\n",
"\n",
"These parameters mean the cutoff in this task is 3.0 Å. We use `data/train.traj` as trainset and `data/test/traj` as testset (actually it should be vaildation dataset here). We set `allowMissing` to `True` since only part of the structures has `virial` property. We train `energy`, `forces`, and `virial` in this task, and the weights are `0.5`, `1.0`, `0.2` respectively. And we just train 10 epoch from previously trained model since it is only an example. (It takes about 15 min on my computer with RTX 2070, you can decrease it to save time)\n",
"\n",
"## Train\n",
"Now, we can train the model by:"
]
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"text": [
"17:58:04 \n",
"\n",
" ) ( ( \n",
" ( /( ) )\\ ) )\\ ) \n",
" )\\()) ( /((()/((()/( \n",
"((_)\\ ( )\\())/(_))/(_)) \n",
" _((_) )\\ (_))/(_)) (_)) \n",
"| || | ((_)| |_ | _ \\| _ \\ \n",
"| __ |/ _ \\| _|| _/| _/ \n",
"|_||_|\\___/ \\__||_| |_| \n",
"HotPP (v.1.0.0 RELEASE)\n",
"\n",
"17:58:04 \n",
"Branch: update_examples\n",
"Commit ID: b11589299d7d8d018201a2cea0d8aa5da0a12bc3\n",
"Commit Message: example tmp\n",
"Diff: \n",
"\n",
"\n",
"17:58:09 Using seed 52614\n",
"17:58:09 Preparing data...\n",
"17:58:11 numWorkers: 8\n",
"17:58:12 Now 0\n",
"17:58:18 Calculating ground energy...\n",
"17:58:18 n_neighbor : 13.572470657917297\n",
"17:58:18 all_elements : [6]\n",
"17:58:18 ground_energy : [-156.75123297795537]\n",
"17:58:18 std : 2.868865634734073\n",
"17:58:18 mean : 0.0\n",
"GPU available: True (cuda), used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"IPU available: False, using: 0 IPUs\n",
"HPU available: False, using: 0 HPUs\n",
"`Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..\n",
"17:58:18 Load checkpoints from model.ckpt\n",
"You are using a CUDA device ('NVIDIA RTX 5880 Ada Generation') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
"Missing logger folder: /home/gegejun/src/hotpp/examples/carbon/outDir/lightning_logs\n",
"Restoring states from the checkpoint path at model.ckpt\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"17:58:20 \n",
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"Equivalent bias : ['models.main.en_equivalent_blocks.0.node_block.graph_conv.radial_fn.radial_fn.freqs', 'models.main.en_equivalent_blocks.0.node_block.graph_conv.radial_fn.mlp.0.bias', 'models.main.en_equivalent_blocks.0.node_block.graph_conv.radial_fn.mlp.2.bias', 'models.main.en_equivalent_blocks.0.node_block.graph_conv.rbf_mixing_list.0.linear.bias', 'models.main.en_equivalent_blocks.0.node_block.graph_conv.rbf_mixing_list.1.linear.bias', 'models.main.en_equivalent_blocks.0.node_block.graph_conv.rbf_mixing_list.2.linear.bias', 'models.main.en_equivalent_blocks.0.node_block.graph_conv.U.linear_list.0.bias', 'models.main.en_equivalent_blocks.0.node_block.self_interact.linear_list.0.bias', 'models.main.en_equivalent_blocks.0.node_block.non_linear.activate_list.0.bias', 'models.main.en_equivalent_blocks.0.node_block.non_linear.activate_list.1.bias', 'models.main.en_equivalent_blocks.0.node_block.non_linear.activate_list.2.bias', 'models.main.en_equivalent_blocks.1.node_block.graph_conv.radial_fn.radial_fn.freqs', 'models.main.en_equivalent_blocks.1.node_block.graph_conv.radial_fn.mlp.0.bias', 'models.main.en_equivalent_blocks.1.node_block.graph_conv.radial_fn.mlp.2.bias', 'models.main.en_equivalent_blocks.1.node_block.graph_conv.rbf_mixing_list.0.linear.bias', 'models.main.en_equivalent_blocks.1.node_block.graph_conv.rbf_mixing_list.1.linear.bias', 'models.main.en_equivalent_blocks.1.node_block.graph_conv.rbf_mixing_list.2.linear.bias', 'models.main.en_equivalent_blocks.1.node_block.graph_conv.U.linear_list.0.bias', 'models.main.en_equivalent_blocks.1.node_block.self_interact.linear_list.0.bias', 'models.main.en_equivalent_blocks.1.node_block.non_linear.activate_list.0.bias', 'models.main.en_equivalent_blocks.1.node_block.non_linear.activate_list.1.bias', 'models.main.en_equivalent_blocks.1.node_block.non_linear.activate_list.2.bias', 'models.main.en_equivalent_blocks.2.node_block.graph_conv.radial_fn.radial_fn.freqs', 'models.main.en_equivalent_blocks.2.node_block.graph_conv.radial_fn.mlp.0.bias', 'models.main.en_equivalent_blocks.2.node_block.graph_conv.radial_fn.mlp.2.bias', 'models.main.en_equivalent_blocks.2.node_block.graph_conv.rbf_mixing_list.0.linear.bias', 'models.main.en_equivalent_blocks.2.node_block.graph_conv.rbf_mixing_list.1.linear.bias', 'models.main.en_equivalent_blocks.2.node_block.graph_conv.rbf_mixing_list.2.linear.bias', 'models.main.en_equivalent_blocks.2.node_block.graph_conv.U.linear_list.0.bias', 'models.main.en_equivalent_blocks.2.node_block.self_interact.linear_list.0.bias', 'models.main.en_equivalent_blocks.2.node_block.non_linear.activate_list.0.bias', 'models.main.en_equivalent_blocks.2.node_block.non_linear.activate_list.1.bias', 'models.main.en_equivalent_blocks.2.node_block.non_linear.activate_list.2.bias', 'models.main.en_equivalent_blocks.3.node_block.graph_conv.radial_fn.radial_fn.freqs', 'models.main.en_equivalent_blocks.3.node_block.graph_conv.radial_fn.mlp.0.bias', 'models.main.en_equivalent_blocks.3.node_block.graph_conv.radial_fn.mlp.2.bias', 'models.main.en_equivalent_blocks.3.node_block.graph_conv.rbf_mixing_list.0.linear.bias', 'models.main.en_equivalent_blocks.3.node_block.graph_conv.rbf_mixing_list.1.linear.bias', 'models.main.en_equivalent_blocks.3.node_block.graph_conv.rbf_mixing_list.2.linear.bias', 'models.main.en_equivalent_blocks.3.node_block.graph_conv.U.linear_list.0.bias', 'models.main.en_equivalent_blocks.3.node_block.self_interact.linear_list.0.bias', 'models.main.en_equivalent_blocks.3.node_block.non_linear.activate_list.0.bias', 'models.main.en_equivalent_blocks.3.node_block.non_linear.activate_list.1.bias', 'models.main.en_equivalent_blocks.3.node_block.non_linear.activate_list.2.bias', 'models.main.en_equivalent_blocks.4.node_block.graph_conv.radial_fn.radial_fn.freqs', 'models.main.en_equivalent_blocks.4.node_block.graph_conv.radial_fn.mlp.0.bias', 'models.main.en_equivalent_blocks.4.node_block.graph_conv.radial_fn.mlp.2.bias', 'models.main.en_equivalent_blocks.4.node_block.graph_conv.rbf_mixing_list.0.linear.bias', 'models.main.en_equivalent_blocks.4.node_block.graph_conv.rbf_mixing_list.1.linear.bias', 'models.main.en_equivalent_blocks.4.node_block.graph_conv.rbf_mixing_list.2.linear.bias', 'models.main.en_equivalent_blocks.4.node_block.graph_conv.U.linear_list.0.bias', 'models.main.en_equivalent_blocks.4.node_block.self_interact.linear_list.0.bias', 'models.main.en_equivalent_blocks.4.node_block.non_linear.activate_list.0.bias']\n",
"Embedding : ['models.main.embedding_layer.z_weights.weight', 'models.main.edge_embedding.linear.weights', 'models.main.edge_embedding.linear.bias']\n",
"Readout : ['models.main.readout_layer.layer_dict.site_energy.activate_fn.weights', 'models.main.readout_layer.layer_dict.site_energy.activate_fn.bias', 'models.main.readout_layer.layer_dict.site_energy.layer1.weights', 'models.main.readout_layer.layer_dict.site_energy.layer1.bias', 'models.main.readout_layer.layer_dict.site_energy.layer2.weights', 'models.main.readout_layer.layer_dict.site_energy.layer2.bias']\n",
"Others : ['models.main.radial_fn.radial_fn.freqs', 'models.main.radial_fn.mlp.0.weight', 'models.main.radial_fn.mlp.0.bias', 'models.main.radial_fn.mlp.2.weight', 'models.main.radial_fn.mlp.2.bias', 'models.ground_energy.one_hot.z_weights.weight']\n",
"\n",
" | Name | Type | Params\n",
"--------------------------------------------\n",
"0 | model | MultiAtomicModule | 368 K \n",
"--------------------------------------------\n",
"368 K Trainable params\n",
"7 Non-trainable params\n",
"368 K Total params\n",
"1.474 Total estimated model params size (MB)\n",
"Restored all states from the checkpoint at model.ckpt\n",
"17:58:23 epoch | step | lr | total | energy | forces | virial \n",
"17:58:23 996 | 254235 | 4.71e-05 | nan / 0.4781 | nan / 0.0104 | nan / 0.4770 | nan / 0.0732 \n",
"18:00:23 997 | 254490 | 4.71e-05 | 0.3883 / 0.3961 | 0.0193 / 0.0179 | 0.3880 / 0.3950 | 0.0092 / 0.0607 \n",
"18:02:45 998 | 254745 | 4.71e-05 | 0.3883 / 0.3966 | 0.0198 / 0.0190 | 0.3880 / 0.3953 | 0.0092 / 0.0653 \n",
"18:05:02 999 | 255000 | 4.71e-05 | 0.3880 / 0.3961 | 0.0196 / 0.0182 | 0.3877 / 0.3949 | 0.0093 / 0.0627 \n",
"18:07:12 1000 | 255255 | 4.71e-05 | 0.3882 / 0.3964 | 0.0192 / 0.0186 | 0.3879 / 0.3951 | 0.0097 / 0.0648 \n",
"18:09:37 1001 | 255510 | 4.71e-05 | 0.3879 / 0.3960 | 0.0191 / 0.0182 | 0.3876 / 0.3948 | 0.0093 / 0.0637 \n",
"18:11:34 1002 | 255765 | 4.71e-05 | 0.3879 / 0.3962 | 0.0194 / 0.0195 | 0.3876 / 0.3949 | 0.0091 / 0.0628 \n",
"18:13:26 1003 | 256020 | 4.71e-05 | 0.3882 / 0.3961 | 0.0195 / 0.0187 | 0.3879 / 0.3948 | 0.0095 / 0.0637 \n",
"18:15:32 1004 | 256275 | 4.71e-05 | 0.3880 / 0.3961 | 0.0194 / 0.0185 | 0.3877 / 0.3949 | 0.0093 / 0.0635 \n",
"18:17:29 1005 | 256530 | 4.71e-05 | 0.3879 / 0.3964 | 0.0195 / 0.0187 | 0.3875 / 0.3951 | 0.0094 / 0.0654 \n",
"`Trainer.fit` stopped: `max_epochs=1006` reached.\n"
]
}
],
"source": [
"! hotpp train --load_checkpoint model.ckpt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also monitor the loss and learning rate curve by\n",
"```bash\n",
"$ tensorboard --logdir=outDir\n",
"```\n",
"during and after training. \n",
"The epoch begin from 996 since the `model.ckpt` stop there, and the training end at 1005 epoch because the `maxEpoch` is 10.\n",
"\n",
"## Eval\n",
"After training done, we can evaluate the model.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mkdir: cannot create directory ‘eval’: File exists\n",
"/home/gegejun/src/hotpp/examples/carbon/eval\n",
"100%|███████████████████████████████████████████| 29/29 [00:04<00:00, 6.67it/s]\n"
]
}
],
"source": [
"%mkdir eval\n",
"%cd eval\n",
"! hotpp eval -m ../outDir/best.pt -d ../data/test.traj -p energy forces virial --device cuda -b 16"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This command means that we use `outDir/best.pt` to evaluate `energy`, `forces`, and `virial` of `data/test.traj` with `cuda`, and the `batchsize` is 16, then the target property and the predicted property are saved by `npy`, you can load them by:\n",
"```python\n",
"import numpy as np\n",
"n_atoms = np.load('n_atoms.npy')\n",
"e_dft = np.load('target_energy.npy')\n",
"e_hot = np.load('output_energy.npy')\n",
"```\n",
"And you can analyze them. To plot them to compare with DFT:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" per_energy : 0.0185 0.0136\n",
" forces : 0.4130 0.3148\n",
" per_virial : 0.0635 0.0445\n"
]
}
],
"source": [
"! hotpp plot -p per_energy forces per_virial"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This command means that we plot `peratom energy`, `forces`, and `peratom virial` calculated by `HotPP` and the target values. And you can see the results: \n",
"\n",
"|`peratom energy`|`forces`|`peratom virial`|\n",
"|:-:|:-:|:-:|\n",
"|![\"perenergy\"](\"eval/per_energy.png\")
|![\"forces\"](\"eval/forces.png\")
|![\"pervirial\"](\"eval/per_virial.png\")
|\n",
"\n",
"\n",
"If you need plot `energy` instead of `peratom energy`, just use:\n",
"```bash\n",
"$ hotpp plot -p energy\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ASE Calculations\n",
"Then we introduce the usage of ASE Calculator interface. \n",
"First, we freeze the model so you can use it in the environment just with `pytorch`, and do not require `hotpp` being installed."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"! hotpp freeze ../outDir/best.pt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This command will give `ase-infer.pt` and `lammps-infer.pt`, we only need `ase-infer.pt` in this example. \n",
"Copy the [ase interface file](https://gitlab.com/bigd4/hotpp/-/blob/master/interface/ase/hotase.py) to the current fold or add `the-path-to-hotase.py` to `PYTHONPATH` so you can imort `Calculator` from this file. And you can use it as an `Calculator` of `ase`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"energy:\n",
" -1266.2486926519648\n",
"forces:\n",
" [[-7.79359758e-15 -1.52816128e-14 8.82974249e-16]\n",
" [ 8.54871729e-15 1.44051437e-14 -1.97247697e-15]\n",
" [ 1.37142901e-14 -5.63988079e-15 -2.05564732e-15]\n",
" [ 6.52256027e-15 -1.86294852e-15 5.46784840e-15]\n",
" [-8.45957548e-15 -2.02962647e-16 -2.51534904e-16]\n",
" [-2.09379735e-15 3.34931735e-15 -2.35922393e-15]\n",
" [-1.40404181e-14 -1.63242222e-15 -1.47104551e-15]\n",
" [ 2.49643098e-15 6.96274635e-15 1.93421668e-15]]\n",
"stress:\n",
" [9.92307113e-02 9.92307113e-02 9.92307113e-02 6.29962891e-16\n",
" 3.00032693e-16 1.77363437e-16]\n"
]
}
],
"source": [
"%cp ../../../interface/ase/hotase.py .\n",
"from hotase import MiaoCalculator\n",
"from ase.build import bulk\n",
"\n",
"atoms = bulk('C', 'diamond', cubic=True)\n",
"calc = MiaoCalculator(\"ase-infer.pt\")\n",
"atoms.calc = calc\n",
"print(f\"\\nenergy:\\n {atoms.get_potential_energy()}\"\n",
" f\"\\nforces:\\n {atoms.get_forces()}\"\n",
" f\"\\nstress:\\n {atoms.get_stress()}\") "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also plot the phono spectrum of diamond with such `Calculator`. This task need `Phonopy`, install it by \n",
"```bash\n",
"$ pip install phonopy\n",
"``` \n",
"if required.\n",
"\n",
"Copy the [phono calculator file](https://gitlab.com/bigd4/hotpp/-/blob/master/tools/hotphono.py) to the current fold or add `the-path-to-phono.py` to `PYTHONPATH` so you can imort functions from this file."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%cp ../../../tools/hotphono.py .\n",
"%cp ../force_constant_dft.npy .\n",
"import hotphono\n",
"import numpy as np\n",
"from phonopy import Phonopy\n",
"from phonopy.interface.phonopy_yaml import PhonopyYaml\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.axes_grid1 import ImageGrid \n",
"\n",
"unitcell = hotphono.ase2phono(atoms)\n",
"supercell_matrix = np.array([2, 2, 2])\n",
"phonon = Phonopy(unitcell=unitcell, \n",
" supercell_matrix=supercell_matrix,\n",
" # primitive_matrix=primitive_matrix,\n",
" primitive_matrix='auto')\n",
"\n",
"force_constants = np.load(\"force_constant_dft.npy\")\n",
"phonon.force_constants = force_constants\n",
"phpy_yaml = PhonopyYaml(settings={'force_sets': True,\n",
" 'displacements': True,\n",
" 'force_constants': True,\n",
" 'born_effective_charge': True,\n",
" 'dielectric_constant': True})\n",
"phpy_yaml.set_phonon_info(phonon)\n",
"atoms.info['phono_info'] = phpy_yaml._data\n",
"band_dict = hotphono.get_band_structure(phonon, atoms)\n",
"fig = plt.figure(figsize=(12, 10))\n",
"axs = ImageGrid(fig, 111, nrows_ncols=(1, len(band_dict['labels_path'])), axes_pad=0.2, label_mode=\"L\")\n",
"#plot_band_structure(band_dict, axs, 'g')\n",
"\n",
"force_constants = np.load(\"force_constant_dft.npy\")\n",
"phonon.force_constants = force_constants\n",
"phpy_yaml = PhonopyYaml(settings={'force_sets': True,\n",
" 'displacements': True,\n",
" 'force_constants': True,\n",
" 'born_effective_charge': True,\n",
" 'dielectric_constant': True})\n",
"phpy_yaml.set_phonon_info(phonon)\n",
"atoms.info['phono_info'] = phpy_yaml._data\n",
"band_dict = hotphono.get_band_structure(phonon, atoms)\n",
"hotphono.plot_band_structure(band_dict, axs, 'green', max_freq=50)\n",
"\n",
"\n",
"force_constants = hotphono.get_force_constants(calc, phonon)\n",
"phonon.force_constants = force_constants\n",
"phpy_yaml = PhonopyYaml(settings={'force_sets': True,\n",
" 'displacements': True,\n",
" 'force_constants': True,\n",
" 'born_effective_charge': True,\n",
" 'dielectric_constant': True})\n",
"phpy_yaml.set_phonon_info(phonon)\n",
"atoms.info['phono_info'] = phpy_yaml._data\n",
"band_dict = hotphono.get_band_structure(phonon, atoms)\n",
"hotphono.plot_band_structure(band_dict, axs, 'blue', linestyle='--', max_freq=50)\n",
"\n",
"\n",
"plt.savefig('phono.pdf')\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "gpu",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}