Tutorial 2: data preparation for deeptb-sk model#
Introduction#
DeePTB is a method that uses deep learning to accelerate first-principles electronic structure simulations.
Version Features#
v1: Constructed tight-binding (TB) models with first-principles accuracy (DeePTB-SK)
v2.0-2.1: Added E3 equivariant networks to represent single-electron operators (Hamiltonian, density matrix, and overlap matrix) (DeePTB-E3)
v2.2: Incorporated built-in SK empirical parameters covering commonly used elements across the periodic table
Through these capabilities, DeePTB provides multiple approaches to accelerate electronic structure simulations of materials.
Learning Objectives#
In this tutorial, you will:
Learn how to prepare data from DFT output to DeePTB-SK data for both abacus and vasp.
Become familiar with the initial training scheme for DeePTB-SK
1. Prepare Data for DeePTB-SK Model#
This section will show how to prepare data for DeePTB-SK model. The training label of DeePTB-SK model is the energy eigenvalue. This tutorial will show the usage of DFTIO with two DFT software, abacus and VASP.
Pre-requisites#
Install DFTIO, see deepmodeling/dftio.git
Run DFT Calculation:
For a given structure, perform static calculations using either ABACUS or VASP. Self-consistent or non-self-consistent calculations are both acceptable. This tutorial will use ABACUS and VASP as examples to prepare data. The following sections will describe the usage of DFTIO for both software.
You can run dftio -h and dftio <command> -h to view the help documentation.
!dftio -h
/usr/bin/sh: 1: dftio: not found
!dftio parse -h
/usr/bin/sh: 1: dftio: not found
1.1 ABACUS Case:#
The following folder contains the result files from ABACUS calculations. We will demonstrate how to convert the ABACUS calculation data into the training data format for the DeePTB-SK model using a single command.
import os
workdir='/root/soft/DeePTB/examples/GaAs_io_sk/data'
os.chdir(f"{workdir}")
!tree -L 1 ./
---------------------------------------------------------------------------
PermissionError Traceback (most recent call last)
Cell In[3], line 3
1 import os
2 workdir='/root/soft/DeePTB/examples/GaAs_io_sk/data'
----> 3 os.chdir(f"{workdir}")
4 get_ipython().system('tree -L 1 ./')
PermissionError: [Errno 13] Permission denied: '/root/soft/DeePTB/examples/GaAs_io_sk/data'
The command to process the data is as follows:
! dftio parse -m abacus -r ./ -p ABACUS -f ase -o abc_ase -eig
/root/dptb_venv/lib/python3.10/site-packages/dpdata/system.py:1106: UserWarning: Data type spins is registered twice; only the newly registered one will be used.
warnings.warn(
Parsing the DFT files: 100%|█████████████████████| 1/1 [00:00<00:00, 368.76it/s]
The command above, the meaning of each parameter is as follows:
-m: Specify the software, can be abacus, vasp, etc., default is abacus-r: root_dir, the root directory of the calculation results-p: prefix, the prefix of the calculation results, will search all file names: The retrieval rule is:glob.glob(root + '/*' + prefix + '*')-f: format output data format: dat ase lmdb; For eigenvalue training data, dat ase is a common format. lmdb is mainly for training quantum operator matrices.-o: out_dir output folder, root_dir/out_dir-eig: Whether to output eigenvalues, add -eig to set it to True
The following command provides a preview of the corresponding eigenvalue band structure, which is convenient for analysis:
!dftio band -r ./abc_ase/AsGa.0 -f ase
# display the band plot:
from IPython.display import Image, display
import os
image_path = f'{workdir}/abc_ase/AsGa.0/band_structure.png'
display(Image(filename=image_path))
Figure(640x480)
The plotting command:
dftio band -r ./abc_ase/AsGa.0 -f ase
The meaning of the parameters is as follows:
-r: root_dir, the root directory of the calculation results. Batch plotting is not supported here, so we directly specify the folder containing the parsed data from the previous step without using the prefix and search folder mode.-f: format, corresponding to the output format during parsing, ase or dat
Band structure analysis#
For the above band structure, we can see that some bands belong to core orbitals. We do not need these bands for training the TB model. We can remove these bands by adding an extra parameter to the above command.
We first check how many bands we want to remove. We can visualize the band structure to see this:
!dftio band -r ./abc_ase/AsGa.0 -f ase -min 5
# display the band plot:
from IPython.display import Image, display
import os
image_path = f'{workdir}/abc_ase/AsGa.0/band_structure.png'
display(Image(filename=image_path))
Figure(640x480)
Note that the above plotting command has an extra parameter:
-minspecifies the starting band for plotting.
For example, -min 5 means starting from the 5th band, as the counting starts from 0. This indicates that the first 5 bands are completely discarded.
Users can change the value of -min to check the band structure. After determining the appropriate value for -min, we can rerun the dftio parse command with this parameter.
Additionally, users should ensure that the chosen -min value does not exceed the total number of bands available in the dataset.
!dftio parse -m abacus -r ./ -p ABACUS -f ase -o abc_ase -eig -min 5
/root/dptb_venv/lib/python3.10/site-packages/dpdata/system.py:1106: UserWarning: Data type spins is registered twice; only the newly registered one will be used.
warnings.warn(
Parsing the DFT files: 100%|█████████████████████| 1/1 [00:00<00:00, 368.47it/s]
The above command will remove the first 5 bands from the band structure plot.
!dftio band -r ./abc_ase/AsGa.0 -f ase # -min 0
# display the band plot:
from IPython.display import Image, display
import os
image_path = f'{workdir}/abc_ase/AsGa.0/band_structure.png'
display(Image(filename=image_path))
Figure(640x480)
Finally, the training data set format for DeePTB-SK model is obtained.#
!tree -L 2 ./abc_ase
./abc_ase
└── AsGa.0
├── band_structure.png
├── eigenvalues.npy
├── kpoints.npy
└── xdat.traj
1 directory, 4 files
1.2. VASP Case:#
The following folder contains the result files from VASP calculations.
workdir='/root/soft/DeePTB/examples/GaAs_io_sk/data/'
os.chdir(f"{workdir}")
! tree ./VASP -L 1
./VASP
├── EIGENVAL
├── KPOINTS
├── OUTCAR
└── POSCAR
0 directories, 4 files
using the same command as in the ABACUS case, but with the -m parameter set to vasp:
! dftio parse -m vasp -r ./ -p VASP -f ase -o vasp_ase -eig
DFTIO WARNING VASP parser only supports the static (SCF or NSCF) calculations. MD and RELAX is not supported yet.
DFTIO WARNING VASP parser only supports the static (SCF or NSCF) calculations. MD and RELAX is not supported yet.
Parsing the DFT files: 100%|██████████████████████| 1/1 [00:00<00:00, 94.65it/s]
Observe that the above command is the same as the abacus case, except for the -m parameter, which is set to vasp.
For visualizing the band structure of data already output in ASE format, the process is similar to that of the ABACUS case.
!dftio band -r ./vasp_ase/AsGa.0 -f ase
# display the band plot:
from IPython.display import Image, display
import os
image_path = f'{workdir}/vasp_ase/AsGa.0/band_structure.png'
display(Image(filename=image_path))
Figure(640x480)
Again, there are core orbitals in the band structure. We can use the same method as in the ABACUS case to remove these bands.
Again, by visualization, we can get the information about how many bands need to be removed:
!dftio band -r ./vasp_ase/AsGa.0 -f ase -min 5
# display the band plot:
from IPython.display import Image, display
import os
image_path = f'{workdir}/vasp_ase/AsGa.0/band_structure.png'
display(Image(filename=image_path))
Figure(640x480)
Re-run the data processing command to discard the lower energy bands. The command is as follows:
! dftio parse -m vasp -r ./ -p VASP -f ase -o vasp_ase -eig -min 5
DFTIO WARNING VASP parser only supports the static (SCF or NSCF) calculations. MD and RELAX is not supported yet.
DFTIO WARNING VASP parser only supports the static (SCF or NSCF) calculations. MD and RELAX is not supported yet.
Parsing the DFT files: 100%|██████████████████████| 1/1 [00:00<00:00, 99.05it/s]
To confirm the band structure, we can visualize it again. The command is as follows:
!dftio band -r ./vasp_ase/AsGa.0 -f ase # -min 5
# display the band plot:
from IPython.display import Image, display
import os
image_path = f'{workdir}/vasp_ase/AsGa.0/band_structure.png'
display(Image(filename=image_path))
Figure(640x480)
Finally, the training data set format for DeePTB-SK model is obtained.#
Note:
After the above operations, the obtained data only contains one frame structure. If you have multiple frame structures, you can perform the following two operations:
Place different structures in different folders, using the same prefix and different suffixes for naming. Different folders should be placed in the same directory. The data structure is as follows:
.root_dir └── prefix.suffix1 ├── info.json ├── eigenvalues.npy ├── kpoints.npy └── xdat.traj └── prefix.suffix2 ├── info.json ├── eigenvalues.npy ├── kpoints.npy └── xdat.traj └── ...
Note: You may notice that I added an
info.jsonfile! This file contains some parameter settings for the training set, which are required when training DeePTB-SK. Please refer to the DeePTB-SK tutorial for details.In another case, if the data for different frames of structures is the same except for the atomic coordinates (e.g., different structures in the same MD trajectory), you can merge them into one file. The
xdat.trajfile is used to save different frames of structures, and the shape ofeigenvalues.npyis [nframe,nk,nb]. The shape ofkpointscan be [nframe,nk,nb] or [nk,nb], where the latter indicates that this structure uses the same kpoints.
2. Training DeePTB-SK Model using vasp data#
In the previous step, we have prepared the data, and now we can start training the model.
import os
workdir='/root/soft/DeePTB/examples/GaAs_io_sk/'
os.chdir(f"{workdir}")
!tree -L 1 ./data/vasp_ase
./data/vasp_ase
└── AsGa.0
1 directory, 0 files
First, we need to create an info.json file in the data folder with the following content:
{
"nframes": 1,
"natoms": 2,
"pos_type": "ase",
"pbc": true,
"bandinfo": {
"band_min": 0,
"band_max": 8,
"emin": null,
"emax": null
}
}
nframes indicates the number of trajectory snapshots, natoms indicates the number of atoms in each snapshot, pos_type indicates the coordinate type, and pbc indicates whether periodic boundary conditions are applied. The bandinfo section contains information about the band window, which can be set according to the user’s needs. The band window information can be sorted by band index or divided by energy range. Note that the value of emin is relative to min(eig[band_min]). Taking min(eig[band_min]) as the 0 energy.
import json
infodict = {
"nframes": 1,
"natoms": 2,
"pos_type": "ase",
"pbc": True,
"bandinfo": {
"band_min": 0,
"band_max": 8,
"emin": None,
"emax": None
}
}
with open(f'{workdir}/data/vasp_ase/AsGa.0/info.json', 'w') as f:
json.dump(infodict, f, indent=4)
2.1 Extract initial empirical SK parameters#
Refer to tutorial 1, we extract the initial SK parameters of GaAs from the built-in baseline model.
os.chdir(f"{workdir}/train")
!dptb esk gaas.json -m poly4
TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.
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# Version: 2.0.4.dev87+5ed8d35 #
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DEEPTB INFO Extracting empirical SK parameters for GaAs
DEEPTB INFO dtype is not provided in the input json, set to the value torch.float32 in model ckpt.
DEEPTB INFO device is not provided in the input json, set to the value cpu in model ckpt.
DEEPTB INFO overlap is not provided in the input json, set to the value True in model ckpt.
DEEPTB INFO Empirical SK parameters are saved in ./sktb.json
DEEPTB INFO If you want to further train the model, please use `dptb config` command to generate input template.
We can compare the initial model band structure with the DFT band structure to see how well the model fits the DFT results.
!dptb run band.json -i sktb.json -o band
# display the band plot:
from IPython.display import Image, display
import os
image_path = f'./band/results/band.png'
display(Image(filename=image_path,width=400))
TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.
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DEEPTB WARNING Warning! structure is not set in run option, read from input config file.
/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)
return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)
DEEPTB INFO KPOINTS klist: 180 kpoints
DEEPTB INFO The eigenvalues are already in data. will use them.
DEEPTB INFO Calculating Fermi energy in the case of spin-degeneracy.
DEEPTB INFO Fermi energy converged after 16 iterations.
DEEPTB INFO q_cal: 7.999999999919238, total_electrons: 8.0, diff q: 8.076206370333239e-11
DEEPTB INFO Estimated E_fermi: -4.927890439324992 based on the valence electrons setting nel_atom : {'As': 5, 'Ga': 3} .
DEEPTB INFO No Fermi energy provided, using estimated value: -4.9279 eV
Figure(640x560)
DEEPTB INFO band calculation successfully completed.
2.1 model training (briefly introduce)#
# 94.116 s on NVIDIA V100
!dptb train input.json -i sktb.json -o nnsk
TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.
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DEEPTB INFO ------------------------------------------------------------------
DEEPTB INFO Cutoff options:
DEEPTB INFO
DEEPTB INFO r_max : {'Ga-Ga': 6.220000000000001, 'Ga-As': 6.43, 'As-Ga': 6.43, 'As-As': 6.630000000000001}
DEEPTB INFO er_max : None
DEEPTB INFO oer_max : None
DEEPTB INFO ------------------------------------------------------------------
/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)
return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)
DEEPTB WARNING The cutoffs in data and model are not checked. be careful!
DEEPTB INFO The ['overlap_param'] are frozen!
DEEPTB INFO The ['overlap_param'] are frozen!
DEEPTB INFO The ['overlap_param'] are frozen!
DEEPTB INFO iteration:1 train_loss: 4.588772 (1.376632) lr: 0.01
DEEPTB INFO checkpoint saved as nnsk.iter1
DEEPTB INFO Epoch 1 summary: train_loss: 4.588772
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DEEPTB INFO checkpoint saved as nnsk.ep1
DEEPTB INFO iteration:2 train_loss: 3.226377 (1.931555) lr: 0.00997
DEEPTB INFO checkpoint saved as nnsk.iter2
DEEPTB INFO Epoch 2 summary: train_loss: 3.226377
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DEEPTB INFO checkpoint saved as nnsk.ep2
DEEPTB INFO iteration:3 train_loss: 2.254159 (2.028337) lr: 0.00994
DEEPTB INFO checkpoint saved as nnsk.iter3
DEEPTB INFO Epoch 3 summary: train_loss: 2.254159
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DEEPTB INFO checkpoint saved as nnsk.ep3
DEEPTB INFO iteration:4 train_loss: 1.622852 (1.906691) lr: 0.00991
DEEPTB INFO checkpoint saved as nnsk.iter4
DEEPTB INFO Epoch 4 summary: train_loss: 1.622852
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DEEPTB INFO checkpoint saved as nnsk.ep4
DEEPTB INFO iteration:5 train_loss: 1.282441 (1.719416) lr: 0.009881
DEEPTB INFO checkpoint saved as nnsk.iter5
DEEPTB INFO Epoch 5 summary: train_loss: 1.282441
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DEEPTB INFO checkpoint saved as nnsk.ep5
DEEPTB INFO iteration:6 train_loss: 1.172433 (1.555321) lr: 0.009851
DEEPTB INFO checkpoint saved as nnsk.iter6
DEEPTB INFO Epoch 6 summary: train_loss: 1.172433
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DEEPTB INFO checkpoint saved as nnsk.ep6
DEEPTB INFO iteration:7 train_loss: 1.205578 (1.450398) lr: 0.009821
DEEPTB INFO checkpoint saved as nnsk.iter7
DEEPTB INFO Epoch 7 summary: train_loss: 1.205578
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DEEPTB INFO iteration:8 train_loss: 1.280451 (1.399414) lr: 0.009792
DEEPTB INFO checkpoint saved as nnsk.iter8
DEEPTB INFO Epoch 8 summary: train_loss: 1.280451
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DEEPTB INFO iteration:9 train_loss: 1.314541 (1.373952) lr: 0.009763
DEEPTB INFO checkpoint saved as nnsk.iter9
DEEPTB INFO Epoch 9 summary: train_loss: 1.314541
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DEEPTB INFO iteration:10 train_loss: 1.271721 (1.343283) lr: 0.009733
DEEPTB INFO checkpoint saved as nnsk.iter10
DEEPTB INFO Epoch 10 summary: train_loss: 1.271721
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DEEPTB INFO iteration:11 train_loss: 1.157573 (1.287570) lr: 0.009704
DEEPTB INFO checkpoint saved as nnsk.iter11
DEEPTB INFO Epoch 11 summary: train_loss: 1.157573
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DEEPTB INFO checkpoint saved as nnsk.ep11
DEEPTB INFO iteration:12 train_loss: 1.003141 (1.202241) lr: 0.009675
DEEPTB INFO checkpoint saved as nnsk.iter12
DEEPTB INFO Epoch 12 summary: train_loss: 1.003141
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DEEPTB INFO checkpoint saved as nnsk.ep12
DEEPTB INFO iteration:13 train_loss: 0.840823 (1.093816) lr: 0.009646
DEEPTB INFO checkpoint saved as nnsk.iter13
DEEPTB INFO Epoch 13 summary: train_loss: 0.840823
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DEEPTB INFO checkpoint saved as nnsk.ep13
DEEPTB INFO iteration:14 train_loss: 0.702213 (0.976335) lr: 0.009617
DEEPTB INFO checkpoint saved as nnsk.iter14
DEEPTB INFO Epoch 14 summary: train_loss: 0.702213
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DEEPTB INFO checkpoint saved as nnsk.ep14
DEEPTB INFO iteration:15 train_loss: 0.604723 (0.864851) lr: 0.009588
DEEPTB INFO checkpoint saved as nnsk.iter15
DEEPTB INFO Epoch 15 summary: train_loss: 0.604723
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DEEPTB INFO checkpoint saved as nnsk.ep15
DEEPTB INFO iteration:16 train_loss: 0.549106 (0.770128) lr: 0.009559
DEEPTB INFO checkpoint saved as nnsk.iter16
DEEPTB INFO Epoch 16 summary: train_loss: 0.549106
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DEEPTB INFO checkpoint saved as nnsk.ep16
DEEPTB INFO iteration:17 train_loss: 0.526850 (0.697144) lr: 0.009531
DEEPTB INFO checkpoint saved as nnsk.iter17
DEEPTB INFO Epoch 17 summary: train_loss: 0.526850
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DEEPTB INFO checkpoint saved as nnsk.ep17
DEEPTB INFO iteration:18 train_loss: 0.521363 (0.644410) lr: 0.009502
DEEPTB INFO checkpoint saved as nnsk.iter18
DEEPTB INFO Epoch 18 summary: train_loss: 0.521363
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DEEPTB INFO checkpoint saved as nnsk.ep18
DEEPTB INFO iteration:19 train_loss: 0.509204 (0.603848) lr: 0.009474
DEEPTB INFO checkpoint saved as nnsk.iter19
DEEPTB INFO Epoch 19 summary: train_loss: 0.509204
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DEEPTB INFO checkpoint saved as nnsk.ep19
DEEPTB INFO iteration:20 train_loss: 0.482992 (0.567591) lr: 0.009445
DEEPTB INFO checkpoint saved as nnsk.iter20
DEEPTB INFO Epoch 20 summary: train_loss: 0.482992
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DEEPTB INFO checkpoint saved as nnsk.ep20
DEEPTB INFO iteration:21 train_loss: 0.445115 (0.530848) lr: 0.009417
DEEPTB INFO checkpoint saved as nnsk.iter21
DEEPTB INFO Epoch 21 summary: train_loss: 0.445115
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DEEPTB INFO checkpoint saved as nnsk.ep21
DEEPTB INFO iteration:22 train_loss: 0.408588 (0.494170) lr: 0.009389
DEEPTB INFO checkpoint saved as nnsk.iter22
DEEPTB INFO Epoch 22 summary: train_loss: 0.408588
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DEEPTB INFO checkpoint saved as nnsk.ep22
DEEPTB INFO iteration:23 train_loss: 0.391567 (0.463389) lr: 0.00936
DEEPTB INFO checkpoint saved as nnsk.iter23
DEEPTB INFO Epoch 23 summary: train_loss: 0.391567
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DEEPTB INFO checkpoint saved as nnsk.ep23
DEEPTB INFO iteration:24 train_loss: 0.396271 (0.443254) lr: 0.009332
DEEPTB INFO checkpoint saved as nnsk.iter24
DEEPTB INFO Epoch 24 summary: train_loss: 0.396271
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DEEPTB INFO iteration:25 train_loss: 0.387768 (0.426608) lr: 0.009304
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DEEPTB INFO iteration:485 train_loss: 0.014498 (0.014524) lr: 0.002336
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DEEPTB INFO Epoch 485 summary: train_loss: 0.014498
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DEEPTB INFO checkpoint saved as nnsk.ep485
DEEPTB INFO iteration:486 train_loss: 0.014487 (0.014513) lr: 0.002329
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DEEPTB INFO Epoch 486 summary: train_loss: 0.014487
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DEEPTB INFO iteration:487 train_loss: 0.014476 (0.014502) lr: 0.002322
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DEEPTB INFO Epoch 487 summary: train_loss: 0.014476
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DEEPTB INFO iteration:488 train_loss: 0.014466 (0.014491) lr: 0.002315
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DEEPTB INFO Epoch 488 summary: train_loss: 0.014466
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DEEPTB INFO iteration:489 train_loss: 0.014455 (0.014480) lr: 0.002308
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DEEPTB INFO Epoch 489 summary: train_loss: 0.014455
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DEEPTB INFO iteration:490 train_loss: 0.014444 (0.014469) lr: 0.002301
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DEEPTB INFO iteration:491 train_loss: 0.014433 (0.014459) lr: 0.002294
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DEEPTB INFO Epoch 491 summary: train_loss: 0.014433
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DEEPTB INFO iteration:492 train_loss: 0.014423 (0.014448) lr: 0.002287
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DEEPTB INFO iteration:496 train_loss: 0.014380 (0.014405) lr: 0.00226
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DEEPTB INFO iteration:497 train_loss: 0.014370 (0.014395) lr: 0.002253
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DEEPTB INFO Epoch 497 summary: train_loss: 0.014370
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DEEPTB INFO checkpoint saved as nnsk.ep497
DEEPTB INFO iteration:498 train_loss: 0.014359 (0.014384) lr: 0.002246
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DEEPTB INFO Epoch 498 summary: train_loss: 0.014359
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DEEPTB INFO iteration:499 train_loss: 0.014349 (0.014374) lr: 0.00224
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DEEPTB INFO Epoch 499 summary: train_loss: 0.014349
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DEEPTB INFO checkpoint saved as nnsk.ep499
DEEPTB INFO iteration:500 train_loss: 0.014339 (0.014363) lr: 0.002233
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DEEPTB INFO Epoch 500 summary: train_loss: 0.014339
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DEEPTB INFO checkpoint saved as nnsk.ep500
DEEPTB INFO finished training
DEEPTB INFO wall time: 94.116 s
!dptb run band.json -i ./nnsk/checkpoint/nnsk.best.pth -o band_train
# !dptb run band.json -i ./ref_ckpt/nnsk_tr1.pth -o band_train
# display the band plot:
from IPython.display import Image, display
import os
image_path = f'./band_train/results/band.png'
display(Image(filename=image_path,width=400))
TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.
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DEEPTB INFO The ['overlap_param'] are frozen!
DEEPTB INFO The ['overlap_param'] are frozen!
DEEPTB WARNING Warning! structure is not set in run option, read from input config file.
/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)
return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)
DEEPTB INFO KPOINTS klist: 180 kpoints
DEEPTB INFO The eigenvalues are already in data. will use them.
DEEPTB INFO Calculating Fermi energy in the case of spin-degeneracy.
DEEPTB WARNING Fermi level bisection did not converge under tolerance 1e-10 after 55 iterations.
DEEPTB INFO q_cal: 8.00000000078933, total_electrons: 8.0, diff q: 7.893294906580195e-10
DEEPTB INFO Estimated E_fermi: -5.508928060531616 based on the valence electrons setting nel_atom : {'As': 5, 'Ga': 3} .
DEEPTB INFO No Fermi energy provided, using estimated value: -5.5089 eV
Figure(640x560)
DEEPTB INFO band calculation successfully completed.
We can also continue training from the previous step’s training results for one more round.
!dptb train input.json -i ./nnsk/checkpoint/nnsk.best.pth -o nnsk2
TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.
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DEEPTB INFO ------------------------------------------------------------------
DEEPTB INFO Cutoff options:
DEEPTB INFO
DEEPTB INFO r_max : {'Ga-Ga': 6.220000000000001, 'Ga-As': 6.43, 'As-Ga': 6.43, 'As-As': 6.630000000000001}
DEEPTB INFO er_max : None
DEEPTB INFO oer_max : None
DEEPTB INFO ------------------------------------------------------------------
/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)
return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)
DEEPTB WARNING The cutoffs in data and model are not checked. be careful!
DEEPTB INFO The ['overlap_param'] are frozen!
DEEPTB INFO The ['overlap_param'] are frozen!
DEEPTB INFO iteration:1 train_loss: 0.014329 (0.004299) lr: 0.01
DEEPTB INFO checkpoint saved as nnsk.iter1
DEEPTB INFO Epoch 1 summary: train_loss: 0.014329
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DEEPTB INFO checkpoint saved as nnsk.ep1
DEEPTB INFO iteration:2 train_loss: 0.015183 (0.007564) lr: 0.00997
DEEPTB INFO checkpoint saved as nnsk.iter2
DEEPTB INFO Epoch 2 summary: train_loss: 0.015183
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DEEPTB INFO iteration:3 train_loss: 0.071114 (0.026629) lr: 0.00994
DEEPTB INFO checkpoint saved as nnsk.iter3
DEEPTB INFO Epoch 3 summary: train_loss: 0.071114
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DEEPTB INFO iteration:4 train_loss: 0.020909 (0.024913) lr: 0.00991
DEEPTB INFO checkpoint saved as nnsk.iter4
DEEPTB INFO Epoch 4 summary: train_loss: 0.020909
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DEEPTB INFO iteration:5 train_loss: 0.056091 (0.034266) lr: 0.009881
DEEPTB INFO checkpoint saved as nnsk.iter5
DEEPTB INFO Epoch 5 summary: train_loss: 0.056091
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DEEPTB INFO iteration:6 train_loss: 0.041297 (0.036376) lr: 0.009851
DEEPTB INFO checkpoint saved as nnsk.iter6
DEEPTB INFO Epoch 6 summary: train_loss: 0.041297
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DEEPTB INFO iteration:7 train_loss: 0.018600 (0.031043) lr: 0.009821
DEEPTB INFO checkpoint saved as nnsk.iter7
DEEPTB INFO Epoch 7 summary: train_loss: 0.018600
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DEEPTB INFO iteration:8 train_loss: 0.026229 (0.029599) lr: 0.009792
DEEPTB INFO checkpoint saved as nnsk.iter8
DEEPTB INFO Epoch 8 summary: train_loss: 0.026229
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DEEPTB INFO iteration:9 train_loss: 0.036438 (0.031650) lr: 0.009763
DEEPTB INFO checkpoint saved as nnsk.iter9
DEEPTB INFO Epoch 9 summary: train_loss: 0.036438
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DEEPTB INFO iteration:10 train_loss: 0.028268 (0.030636) lr: 0.009733
DEEPTB INFO checkpoint saved as nnsk.iter10
DEEPTB INFO Epoch 10 summary: train_loss: 0.028268
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DEEPTB INFO iteration:11 train_loss: 0.018145 (0.026888) lr: 0.009704
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DEEPTB INFO Epoch 11 summary: train_loss: 0.018145
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DEEPTB INFO iteration:12 train_loss: 0.019613 (0.024706) lr: 0.009675
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DEEPTB INFO iteration:13 train_loss: 0.024548 (0.024659) lr: 0.009646
DEEPTB INFO checkpoint saved as nnsk.iter13
DEEPTB INFO Epoch 13 summary: train_loss: 0.024548
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DEEPTB INFO iteration:14 train_loss: 0.021809 (0.023804) lr: 0.009617
DEEPTB INFO checkpoint saved as nnsk.iter14
DEEPTB INFO Epoch 14 summary: train_loss: 0.021809
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DEEPTB INFO iteration:15 train_loss: 0.016186 (0.021519) lr: 0.009588
DEEPTB INFO checkpoint saved as nnsk.iter15
DEEPTB INFO Epoch 15 summary: train_loss: 0.016186
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DEEPTB INFO iteration:16 train_loss: 0.016139 (0.019905) lr: 0.009559
DEEPTB INFO checkpoint saved as nnsk.iter16
DEEPTB INFO Epoch 16 summary: train_loss: 0.016139
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DEEPTB INFO iteration:17 train_loss: 0.019393 (0.019751) lr: 0.009531
DEEPTB INFO checkpoint saved as nnsk.iter17
DEEPTB INFO Epoch 17 summary: train_loss: 0.019393
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DEEPTB INFO iteration:18 train_loss: 0.019047 (0.019540) lr: 0.009502
DEEPTB INFO checkpoint saved as nnsk.iter18
DEEPTB INFO Epoch 18 summary: train_loss: 0.019047
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DEEPTB INFO iteration:19 train_loss: 0.015228 (0.018246) lr: 0.009474
DEEPTB INFO checkpoint saved as nnsk.iter19
DEEPTB INFO Epoch 19 summary: train_loss: 0.015228
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DEEPTB INFO iteration:20 train_loss: 0.013449 (0.016807) lr: 0.009445
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DEEPTB INFO Epoch 25 summary: train_loss: 0.012451
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DEEPTB INFO iteration:26 train_loss: 0.014118 (0.014186) lr: 0.009276
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DEEPTB INFO Epoch 26 summary: train_loss: 0.014118
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DEEPTB INFO iteration:27 train_loss: 0.014291 (0.014218) lr: 0.009249
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DEEPTB INFO Epoch 27 summary: train_loss: 0.014291
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DEEPTB INFO iteration:28 train_loss: 0.012677 (0.013756) lr: 0.009221
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DEEPTB INFO Epoch 28 summary: train_loss: 0.012677
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DEEPTB INFO iteration:29 train_loss: 0.011762 (0.013158) lr: 0.009193
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DEEPTB INFO Epoch 29 summary: train_loss: 0.011762
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DEEPTB INFO Epoch 30 summary: train_loss: 0.012451
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DEEPTB INFO iteration:31 train_loss: 0.012948 (0.012946) lr: 0.009138
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DEEPTB INFO Epoch 31 summary: train_loss: 0.012948
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DEEPTB INFO iteration:32 train_loss: 0.012087 (0.012688) lr: 0.009111
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DEEPTB INFO Epoch 32 summary: train_loss: 0.012087
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DEEPTB INFO iteration:33 train_loss: 0.011156 (0.012229) lr: 0.009083
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DEEPTB INFO Epoch 33 summary: train_loss: 0.011156
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DEEPTB INFO iteration:34 train_loss: 0.011445 (0.011994) lr: 0.009056
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DEEPTB INFO Epoch 34 summary: train_loss: 0.011445
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DEEPTB INFO iteration:35 train_loss: 0.012089 (0.012022) lr: 0.009029
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DEEPTB INFO Epoch 35 summary: train_loss: 0.012089
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DEEPTB INFO Epoch 36 summary: train_loss: 0.011747
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DEEPTB INFO iteration:37 train_loss: 0.010815 (0.011602) lr: 0.008975
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DEEPTB INFO Epoch 37 summary: train_loss: 0.010815
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DEEPTB INFO iteration:38 train_loss: 0.010647 (0.011316) lr: 0.008948
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DEEPTB INFO Epoch 38 summary: train_loss: 0.010647
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DEEPTB INFO iteration:39 train_loss: 0.011178 (0.011274) lr: 0.008921
DEEPTB INFO checkpoint saved as nnsk.iter39
DEEPTB INFO Epoch 39 summary: train_loss: 0.011178
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DEEPTB INFO iteration:40 train_loss: 0.011241 (0.011264) lr: 0.008894
DEEPTB INFO checkpoint saved as nnsk.iter40
DEEPTB INFO Epoch 40 summary: train_loss: 0.011241
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DEEPTB INFO iteration:41 train_loss: 0.010653 (0.011081) lr: 0.008868
DEEPTB INFO checkpoint saved as nnsk.iter41
DEEPTB INFO Epoch 41 summary: train_loss: 0.010653
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DEEPTB INFO checkpoint saved as nnsk.iter42
DEEPTB INFO Epoch 42 summary: train_loss: 0.010307
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DEEPTB INFO Epoch 43 summary: train_loss: 0.010484
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DEEPTB INFO checkpoint saved as nnsk.iter44
DEEPTB INFO Epoch 44 summary: train_loss: 0.010549
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DEEPTB INFO iteration:45 train_loss: 0.010264 (0.010557) lr: 0.008762
DEEPTB INFO checkpoint saved as nnsk.iter45
DEEPTB INFO Epoch 45 summary: train_loss: 0.010264
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DEEPTB INFO iteration:46 train_loss: 0.010057 (0.010407) lr: 0.008735
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DEEPTB INFO Epoch 46 summary: train_loss: 0.010057
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DEEPTB INFO iteration:47 train_loss: 0.010127 (0.010323) lr: 0.008709
DEEPTB INFO checkpoint saved as nnsk.iter47
DEEPTB INFO Epoch 47 summary: train_loss: 0.010127
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DEEPTB INFO iteration:48 train_loss: 0.010151 (0.010271) lr: 0.008683
DEEPTB INFO checkpoint saved as nnsk.iter48
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DEEPTB INFO iteration:49 train_loss: 0.009934 (0.010170) lr: 0.008657
DEEPTB INFO checkpoint saved as nnsk.iter49
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DEEPTB INFO iteration:55 train_loss: 0.009474 (0.009639) lr: 0.008502
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DEEPTB INFO Epoch 55 summary: train_loss: 0.009474
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DEEPTB INFO Epoch 56 summary: train_loss: 0.009481
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DEEPTB INFO iteration:57 train_loss: 0.009336 (0.009515) lr: 0.008451
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DEEPTB INFO iteration:58 train_loss: 0.009205 (0.009422) lr: 0.008426
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DEEPTB INFO Epoch 58 summary: train_loss: 0.009205
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DEEPTB INFO Epoch 60 summary: train_loss: 0.009177
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DEEPTB INFO Epoch 61 summary: train_loss: 0.009059
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DEEPTB INFO iteration:62 train_loss: 0.008968 (0.009150) lr: 0.008325
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DEEPTB INFO Epoch 62 summary: train_loss: 0.008968
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DEEPTB INFO iteration:499 train_loss: 0.003410 (0.003415) lr: 0.00224
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DEEPTB INFO iteration:500 train_loss: 0.003408 (0.003413) lr: 0.002233
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DEEPTB INFO Epoch 500 summary: train_loss: 0.003408
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DEEPTB INFO finished training
DEEPTB INFO wall time: 93.925 s
!dptb run band.json -i ./nnsk2/checkpoint/nnsk.best.pth -o band_train
#!dptb run band.json -i ./ref_ckpt/nnsk_tr2.pth -o band_train
# display the band plot:
from IPython.display import Image, display
import os
image_path = f'./band_train/results/band.png'
display(Image(filename=image_path,width=400))
TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.
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DEEPTB INFO The ['overlap_param'] are frozen!
DEEPTB INFO The ['overlap_param'] are frozen!
DEEPTB WARNING Warning! structure is not set in run option, read from input config file.
/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)
return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)
DEEPTB INFO KPOINTS klist: 180 kpoints
DEEPTB INFO The eigenvalues are already in data. will use them.
DEEPTB INFO Calculating Fermi energy in the case of spin-degeneracy.
DEEPTB WARNING Fermi level bisection did not converge under tolerance 1e-10 after 55 iterations.
DEEPTB INFO q_cal: 8.000000003908374, total_electrons: 8.0, diff q: 3.90837406882838e-09
DEEPTB INFO Estimated E_fermi: -5.590590238571167 based on the valence electrons setting nel_atom : {'As': 5, 'Ga': 3} .
DEEPTB INFO No Fermi energy provided, using estimated value: -5.5906 eV
Figure(640x560)
DEEPTB INFO band calculation successfully completed.
Author: Gu, Qiangqiang : guqq@ustc.edu.cn
Thank you for reading!
For more information about training, please refer to tutorial 3.