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https://github.com/deepmodeling/deepks-kit

a package for developing machine learning-based chemically accurate energy and density functional models
https://github.com/deepmodeling/deepks-kit

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a package for developing machine learning-based chemically accurate energy and density functional models

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README 

        
# DeePKS-kit



DeePKS-kit is a program to generate accurate energy functionals for quantum chemistry systems,

for both perturbative scheme (DeePHF) and self-consistent scheme (DeePKS).



The program provides a command line interface `deepks` that contains five sub-commands, 

- `train`: train an neural network based post-HF energy functional model

- `test`: test the post-HF model with given data and show statistics

- `scf`: run self-consistent field calculation with given energy model

- `stats`: collect and print statistics of the SCF the results

- `iterate`: iteratively train an self-consistent model by combining four commands above



## Installation



DeePKS-kit is a pure python library so it can be installed following the standard `git clone` then `pip install` procedure. Note that the two main requirements `pytorch` and `pyscf` will not be installed automatically so you will need to install them manually in advance. Below is a more detailed instruction that includes installing the required libraries in the environment.



We use `conda` here as an example. So first you may need to install [Anaconda](https://docs.anaconda.com/anaconda/install/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html).



To reduce the possibility of library conflicts, we suggest create a new environment (named `deepks`) with basic dependencies installed (optional):

```bash

conda create -n deepks numpy scipy h5py ruamel.yaml paramiko

conda activate deepks

```

Now you are in the new environment called `deepks`.

Next, install [PyTorch](https://pytorch.org/get-started/locally/) 

```bash

# assuming a GPU with cudatoolkit 10.2 support

conda install pytorch cudatoolkit=10.2 -c pytorch

```

and [PySCF](https://github.com/pyscf/pyscf).

```bash

# the conda package does not support python >= 3.8 so we use pip

pip install pyscf

```



Once the environment has been setup properly, using pip to install DeePKS-kit:

```bash

pip install git+https://github.com/deepmodeling/deepks-kit/

```



## Usage



An relatively detailed decrisption of the `deepks-kit` library can be found in [here](https://arxiv.org/pdf/2012.14615.pdf). Please also refer to the reference for the description of methods.



Please see [`examples`](./examples) folder for the usage of `deepks-kit` library. A detailed example with executable data for single water molecules can be found [here](./examples/water_single). A more complicated one for training water clusters can be found [here](./examples/water_cluster).



Check [this input file](./examples/water_cluster/args.yaml) for detailed explanation for possible input parameters, and also [this one](./examples/water_cluster/shell.yaml) if you would like to run on local machine instead of using Slurm scheduler.



## References



[1] Chen, Y., Zhang, L., Wang, H. and E, W., 2020. Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy. The Journal of Physical Chemistry A, 124(35), pp.7155-7165.



[2] Chen, Y., Zhang, L., Wang, H. and E, W., 2021. DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory. Journal of Chemical Theory and Computation, 17(1), pp.170–181.