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https://github.com/deepmodeling/DeepH-pack

Deep neural networks for density functional theory Hamiltonian.
https://github.com/deepmodeling/DeepH-pack

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Deep neural networks for density functional theory Hamiltonian.

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README 

        


  




--------------------------------------------------------------------------------

[![DOI:10.1038/s43588-022-00265-6](https://zenodo.org/badge/DOI/10.1038/s43588-022-00265-6.svg)](https://doi.org/10.1038/s43588-022-00265-6)

[![Documentation Status](https://readthedocs.org/projects/deeph-pack/badge/)](https://deeph-pack.readthedocs.io/)



DeepH-pack is the official implementation of the DeepH

(**Deep** **H**amiltonian) method described in the paper

[*Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation*](https://www.nature.com/articles/s43588-022-00265-6)

and in the [Research Briefing](https://www.nature.com/articles/s43588-022-00270-9).



DeepH-pack supports DFT results made by

[ABACUS](https://abacus.ustc.edu.cn/), [OpenMX](http://www.openmx-square.org/), 

[FHI-aims](https://fhi-aims.org/) or [SIESTA](https://departments.icmab.es/leem/siesta/) and will support

[HONPAS](http://honpas.ustc.edu.cn/) soon.



For more information, see the

[documentation](https://deeph-pack.readthedocs.io) and the

[talk (in Chinese)](https://www.bilibili.com/video/BV1wg41127Mm?share_source=copy_web&vd_source=97d03cee69ef354790abb6105b612e86).



# Contents

1. [How to cite](#how-to-cite)

1. [Requirements](#requirements)

1. [Usage](#usage)

1. [Demo](#demo-deeph-study-on-twisted-bilayer-bismuthene)

1. [Team](#team)



## How to cite



```

@article{deeph,

   author = {Li, He and Wang, Zun and Zou, Nianlong and Ye, Meng and Xu, Runzhang and Gong, Xiaoxun and Duan, Wenhui and Xu, Yong},

   title = {Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation},

   journal = {Nature Computational Science},

   volume = {2},

   number = {6},

   pages = {367-377},

   ISSN = {2662-8457},

   DOI = {10.1038/s43588-022-00265-6},

   url = {https://doi.org/10.1038/s43588-022-00265-6},

   year = {2022},

   type = {Journal Article}

}

```



### Recent development

```

@article{deephe3,

   author = {Gong, Xiaoxun and Li, He and Zou, Nianlong and Xu, Runzhang and Duan, Wenhui and Xu, Yong},

   title = {General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian},

   journal = {Nature Communications},

   volume = {14},

   number = {1},

   pages = {2848},

   ISSN = {2041-1723},

   DOI = {10.1038/s41467-023-38468-8},

   url = {https://doi.org/10.1038/s41467-023-38468-8},

   year = {2023},

   type = {Journal Article}

}



@article{xdeeph,

   author = {Li, He and Tang, Zechen and Gong, Xiaoxun and Zou, Nianlong and Duan, Wenhui and Xu, Yong},

   title = {Deep-learning electronic-structure calculation of magnetic superstructures},

   journal = {Nature Computational Science},

   volume = {3},

   number = {4},

   pages = {321-327},

   ISSN = {2662-8457},

   DOI = {10.1038/s43588-023-00424-3},

   url = {https://doi.org/10.1038/s43588-023-00424-3},

   year = {2023},

   type = {Journal Article}

}

```



## Requirements



To use DeepH-pack, following environments and packages are required：



### Python

Prepare the Python 3.9 interpreter. Install the following Python packages required:

- NumPy

- SciPy

- PyTorch = 1.9.1

- PyTorch Geometric = 1.7.2

- e3nn = 0.3.5

- pymatgen

- h5py

- TensorBoard

- pathos

- psutil



In Linux, you can quickly achieve the requirements by running



```bash

# install miniconda with python 3.9

wget https://repo.anaconda.com/miniconda/Miniconda3-py39_4.10.3-Linux-x86_64.sh

bash Miniconda3-py39_4.10.3-Linux-x86_64.sh



# install packages by conda

conda install numpy

conda install scipy

conda install pytorch==1.9.1 ${pytorch_config}

conda install pytorch-geometric=1.7.2 -c rusty1s -c conda-forge

conda install pymatgen -c conda-forge



# install packages by pip

pip install e3nn==0.3.5

pip install h5py

pip install tensorboard

pip install pathos

pip install psutil

```



with `${pytorch_config}` replaced by your own configuration. 

You can find how to set it in [the official website of PyTorch](https://pytorch.org/get-started/previous-versions/).

### Julia

Prepare the Julia 1.6.6 interpreter. Install the following Julia packages required with Julia's builtin package manager:

- Arpack.jl

- HDF5.jl

- ArgParse.jl

- JLD.jl

- JSON.jl

- IterativeSolvers.jl

- DelimitedFiles.jl

- StaticArrays.jl

- LinearMaps.jl

- Pardiso.jl



In Linux, you can quickly achieve the requirements by first running

```bash

# install julia 1.6.6

wget https://julialang-s3.julialang.org/bin/linux/x64/1.6/julia-1.6.6-linux-x86_64.tar.gz

tar xzvf julia-1.6.6-linux-x86_64.tar.gz



# open the julia REPL

julia

```

Then enter the pkg REPL by pressing `]` from the Julia REPL. In the pkg REPL run

```julia

(@v1.6) pkg> add Arpack

(@v1.6) pkg> add HDF5

(@v1.6) pkg> add ArgParse

(@v1.6) pkg> add JLD

(@v1.6) pkg> add JSON

(@v1.6) pkg> add IterativeSolvers

(@v1.6) pkg> add DelimitedFiles

(@v1.6) pkg> add StaticArrays

(@v1.6) pkg> add LinearMaps

```

Follow [these instructions](https://github.com/JuliaSparse/Pardiso.jl#mkl-pardiso) to install Pardiso.jl.



### One of the supported DFT packages

One of the supported DFT packages is required to obtain the dataset and

calculate the overlap matrix for large-scale material systems.

DeepH-pack supports DFT results made by ABACUS, OpenMX, FHI-aims

or SIESTA and will support HONPAS soon.



1. **ABACUS**: Install [ABACUS package](https://abacus.ustc.edu.cn)

    for density functional theory Hamiltonian matrix calculation to

    construct datasets. DeepH-pack requires

    [ABACUS version >= 2.3.2](https://github.com/deepmodeling/abacus-develop/releases/tag/v2.3.2).

2. **OpenMX**:

    1. Install [OpenMX package version 3.9](http://www.openmx-square.org/download.html) for density functional theory Hamiltonian matrix calculation to construct datasets.

        If you are using Intel MKL and Intel MPI environments, you can use the following variable definitions for makefile

        ```

        CC = mpiicc -O3 -xHOST -ip -no-prec-div -qopenmp -I${MKLROOT}/include/fftw -I${MKLROOT}/include

        FC = mpiifort -O3 -xHOST -ip -no-prec-div -qopenmp -I${MKLROOT}/include

        LIB = ${CMPLR_ROOT}/linux/compiler/lib/intel64_lin/libiomp5.a ${MKLROOT}/lib/intel64/libmkl_blas95_lp64.a ${MKLROOT}/lib/intel64/libmkl_lapack95_lp64.a ${MKLROOT}/lib/intel64/libmkl_scalapack_lp64.a -Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_lp64.a ${MKLROOT}/lib/intel64/libmkl_intel_thread.a ${MKLROOT}/lib/intel64/libmkl_core.a ${MKLROOT}/lib/intel64/libmkl_blacs_intelmpi_lp64.a -Wl,--end-group ${CMPLR_ROOT}/linux/compiler/lib/intel64_lin/libifcoremt.a -lpthread -lm -ldl

        ```

        Or edit the makefile yourself according to your environment to install OpenMX version 3.9.

    2. A modified OpenMX package is also used to compute overlap matrices only for large-scale materials structure. Install 'overlap only' OpenMX according to the *readme* documentation in this [repository](https://github.com/mzjb/overlap-only-OpenMX).

3. **SIESTA**:

    Install [SIESTA package](https://gitlab.com/siesta-project/siesta) for

    density functional theory Hamiltonian matrix calculation to construct

    datasets. DeepH-pack requires SIESTA version >= 4.1.5.

## Usage



### Install DeepH-pack

Run the following command in the path of DeepH-pack:

```bash

git clone https://github.com/mzjb/DeepH-pack.git

cd DeepH-pack

pip install .

```



### Prepare the dataset

To perform efficient *ab initio* electronic structure calculation by DeepH method 

for a class of large-scale material systems, one needs to design an appropriate 

dataset of small structures that have close chemical bonding environment with 

the target large-scale material systems. Therefore, the first step of a DeepH 

study is to perform the DFT calculation on the above dataset to get the DFT 

Hamiltonian matrices with the localized basis. DeepH-pack supports DFT 

results made by ABACUS, OpenMX, FHI-aims or SIESTA and will support HONPAS soon.



For more information, see the

[documentation](https://deeph-pack.readthedocs.io/en/latest/dataset/dataset.html).



### Preprocess the dataset

`Preprocess` is a part of DeepH-pack. Through `Preprocess`, DeepH-pack will

convert the unit of physical quantity, store the data files in the format

of text and *HDF5* for each structure in a separate folder, generate local

coordinates, and perform basis transformation for DFT Hamiltonian matrices.

We use the following convention of units:



Quantity | Unit 

---|---

Length   | Å    

Energy   | eV   



You need to edit a configuration in the format of *ini*, setting up the

file referring to the default file `DeepH-pack/deeph/preprocess/preprocess_default.ini`.

The meaning of the keywords can be found in the

[documentation](https://deeph-pack.readthedocs.io/en/latest/keyword/preprocess.html).

For a quick start, you must set up *raw_dir*, *processed_dir* and *interface*.



With the configuration file prepared, run 

```bash

deeph-preprocess --config ${config_path}

```

with `${config_path}` replaced by the path of your configuration file.



### Train your model

`Train` is a part of DeepH-pack, which is used to train a deep learning model using the processed dataset.



Prepare a configuration in the format of *ini*, setting up the file referring to the default `DeepH-pack/deeph/default.ini`. The meaning of the keywords can be found in the [documentation](https://deeph-pack.readthedocs.io/en/latest/keyword/train.html). For a quick start, you must set up *graph_dir*, *save_dir*, *raw_dir* and *orbital*, other keywords can stay default and be adjusted later.



With the configuration file prepared, run 

```bash

deeph-train --config ${config_path}

```

with `${config_path}` replaced by the path of your configuration file.



Tips:

- **Name your dataset**. Use *dataset_name* to name your dataset, the same names may overwrite each other.



- **Hyperparameters of the neural network**. The neural network here contains some hyperparameters. For a specific problem your should try adjusting the hyperparameters to obtain better results.



- **The keyword *orbital***. The keyword *orbital* states which orbitals or matrix elements are predicted. It is a little complicated to understand its data structure. To figure out it, you can refer to the [documentation](https://deeph-pack.readthedocs.io/en/latest/keyword/train.html#:~:text=generate%20crystal%20graphs.-,orbital,-%3A%20A%20JSON%20format) or the method [make_mask](https://github.com/mzjb/DeepH-pack/blob/main/deeph/kernel.py#:~:text=def%20make_mask(self%2C%20dataset)%3A) in class `DeepHKernel` defined in `DeepH-pack/deeph/kernel.py`.



    Alternatively, a Python script at `DeepH-pack/tools/get_all_orbital_str.py` can be used to generate a default configuration to predict all orbitals with one model.



- **Use TensorBoard for visualizations**. You can track and visualize the training process through TensorBoard by running

  ```bash

  tensorboard --logdir=./tensorboard

  ```

  in the output directory (*save_dir*):



### Inference with your model

`Inference` is a part of DeepH-pack, which is used to predict the 

DFT Hamiltonian for large-scale material structures and perform 

sparse calculation of physical properties.



Firstly, one should prepare the structure file of large-scale material 

and calculate the overlap matrix. Overlap matrix calculation does not

require `SCF`. Even if the material system is large, only a small calculation

time and memory consumption are required. Following are the steps to

calculate the overlap matrix using different supported DFT packages:

1. **ABACUS**: Set the following parameters in the input file of ABACUS `INPUT`:

    ```

    calculation   get_S

    ```

    and run ABACUS like a normal `SCF` calculation.

    [ABACUS version >= 2.3.2](https://github.com/deepmodeling/abacus-develop/releases/tag/v2.3.2) is required.

2. **OpenMX**: See this [repository](https://github.com/mzjb/overlap-only-OpenMX#usage).



For overlap matrix calculation, you need to use the same basis set and DFT

software when preparing the dataset.



Then, prepare a configuration in the format of *ini*, setting up the 

file referring to the default `DeepH-pack/deeph/inference/inference_default.ini`. 

The meaning of the keywords can be found in the

[INPUT KEYWORDS section](https://deeph-pack.readthedocs.io/en/latest/keyword/inference.html). 

For a quick start, you must set up *OLP_dir*, *work_dir*, *interface*,

*trained_model_dir* and *sparse_calc_config*, as well as a `JSON` 

configuration file located at *sparse_calc_config* for sparse calculation.



With the configuration files prepared, run 

```bash

deeph-inference --config ${config_path}

```

with `${config_path}` replaced by the path of your configuration file.



## Demo: DeepH study on twisted bilayer bismuthene

When the directory structure of the code folder is not modified, the scripts in it can be used to generate a dataset of non-twisted structures, train a DeepH model, make predictions on the DFT Hamiltonian matrix of twisted structure, and perform sparse diagonalization to compute the band structure for the example study of bismuthene.



Firstly, generate example input files according to your environment path by running the following command:

```bash

cd DeepH-pack

python gen_example.py ${openmx_path} ${openmx_overlap_path} ${pot_path} ${python_interpreter} ${julia_interpreter}

```

with `${openmx_path}`, `${openmx_overlap_path}`, `${pot_path}`, `${python_interpreter}`, and `${julia_interpreter}` replaced by the path of original OpenMX executable program, modified 'overlap only' OpenMX executable program, VPS and PAO directories of OpenMX, Python interpreter, and Julia interpreter, respectively. For example, 

```bash

cd DeepH-pack

python gen_example.py /home/user/openmx/source/openmx /home/user/openmx_overlap/source/openmx /home/user/openmx/DFT_DATA19 python /home/user/julia-1.5.4/bin/julia

```



Secondly, enter the generated `example/` folder and run `run.sh` in each folder one-by-one from 1 to 5. Please note that `run.sh` should be run in the directory where the `run.sh` file is located.

```bash

cd example/1_DFT_calculation

bash run.sh

cd ../2_preprocess

bash run.sh

cd ../3_train

bash run.sh

cd ../4_compute_overlap

bash run.sh

cd ../5_inference

bash run.sh

```

The third step, the neural network training process, is recommended to be carried out on the GPU. In addition, in order to get the energy band faster, it is recommended to calculate the eigenvalues ​​of different k points in parallel in the fifth step by *which_k* interface.



After completing the calculation, you can find the band structure data in OpenMX Band format of twisted bilayer bismuthene with 244 atoms per supercell computed by the predicted DFT Hamiltonian in the file below:

```

example/work_dir/inference/5_4/openmx.Band

```

The plotted band structure will be consistent with the right pannel of figure 6c in our paper.



## Demo: Reproduce the experimental results of the paper

You can train DeepH models using the existing [dataset](https://zenodo.org/record/6555484) to reproduce the results of our paper.



Firstly, download the processed dataset for graphene (*graphene_dataset.zip*), MoS2 (*MoS2_dataset.zip*), twisted bilayer graphene (*TBG_dataset.zip*) or twisted bilayer bismuthene (*TBB_dataset.zip*). Uncompress the ZIP file.



Secondly, edit corresponding config files in the `DeepH-pack/ini/`. *raw_dir* should be set to the path of the downloaded dataset. *graph_dir* and *save_dir* should be set to the path to save your graph file and results file during the training. For grahene, twisted bilayer graphene and twisted bilayer bismuthene, a single MPNN model is used for each dataset. For MoS2, four MPNN models are used. Run 

```bash

deeph-train --config ${config_path}

```

with `${config_path}` replaced by the path of config file for training.



After completing the training, you can find the trained model in *save_dir*, which can be used to make prediction on new structures by run

```bash

deeph-inference --config ${inference_config_path}

```

with `${inference_config_path}` replaced by the path of config file for inference.

Please note that the DFT results in this dataset were calculated using OpenMX.

This means that if you want to use a model trained on this dataset to calculate properties, you need to use the overlap calculated using OpenMX.

The orbital information required for overlap calculations can be found in the [paper](https://www.nature.com/articles/s43588-022-00265-6).



## Demo: Train the DeepH model using the ABACUS interface

Train the DeepH model by random graphene supercells

and predict the Hamiltonian of carbon nanotube using

the ABACUS interface. See README.md in

[this file](https://github.com/deepmodeling/DeepH-pack/files/9526304/demo_abacus.zip)

for details.



## Team

#### Main developers

- **He Li**



   Tsinghua University



   mzjb313 [at] gmail [dot] com



+ **Zechen Tang**

   

    Tsinghua University



#### Collaborators

+ **Xiaoxun Gong**



- **Honggeng Tao**



+ **Zun Wang**



- **Nianlong Zou**



+ **Ting Bao**



#### Supervisors

- **Yong Xu**



   Tsinghua University



   yongxu [at] tsinghua [dot] edu [dot] cn



+ **Wenhui Duan**



   Tsinghua University



   duanw [at] tsinghua [dot] edu [dot] cn