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 or Miniconda.

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

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

# assuming a GPU with cudatoolkit 10.2 support
conda install pytorch cudatoolkit=10.2 -c pytorch

and PySCF.

# 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:

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

Usage

An relatively detailed decrisption of the deepks-kit library can be found in here. Please also refer to the reference for the description of methods.

Please see examples folder for the usage of deepks-kit library. A detailed example with executable data for single water molecules can be found here. A more complicated one for training water clusters can be found here.

Check this input file for detailed explanation for possible input parameters, and also this one 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.