# DeePKS-kit # 模型介绍 DeePKS-kit是一个为量子化学系统生成精确能量泛函的程序 # 模型结构 DeePKS-kit架构和工作流程的示意图[1]。上图：整个迭代学习过程的主要步骤。左下：神经网络（NN）能量泛数的训练。描述符由给定的分子轨道计算出来，并用作神经网络模型的输入。使用PyTorch库实现了随机梯度体面（SGD）训练。右下：求解广义的Kohn-Sham自洽场（自洽场）方程。XC电位是根据训练后的神经网络函数计算出来的。该求解器被实现为PySCF库的一个新类。 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 # 数据集 ## 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.