# A Deep Learning-Augmented Density Functional Framework for Reaction Modeling with Chemical Accuracy this manual will tell you how to train and test with you datasets. ## Install Requirements Install package version ```python python==3.9.0 pyscf==2.2.1 torch==2.0.0 ruamel.yaml==0.17.21 numpy==1.24.2 scipy==1.10.1 paramiko==3.1.0 ``` Then we will install package [DeePKS-kit](https://github.com/deepmodeling/deepks-kit). 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 `mamba` here as an example. So first you may need to install [Miniforge](https://github.com/conda-forge/miniforge). and install the requirement package. ``` mamba create -n deepks python=3.9.0 paramiko=3.1.0 numpy scipy=1.10.1 h5py ruamel.yaml=0.17.21 paramiko=3.1.0 mamba activate deepks pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 pip install pyscf=2.2.1 ``` Then you can install deepks ``` git clone https://github.com/deepmodeling/deepks-kit cd deepks/ python setup.py install ``` or you can install ``` pip install git+https://github.com/deepmodeling/deepks-kit/ ``` ## Train our datasets The project is Below, `QM` document contain the each type DFT training parameters`train_input.yaml` and datasets path such as `train.raw`, and `get_energy.py` will give you output energy. `test_sets` contain the descriptor of test sets and reults `validate_sets` contain the results ```python projects ├── QM │ ├── B3LYP │ │ ├── GRAM │ │ ├── GRAMandT1X │ │ └── T1X └── validate_sets └── WHG_BHRE └── result ``` ### Train we use the `DeePHF@B3LYP` as a example. ``` cd QM/B3LYP/GRAMandT1X deepks train train_input.yaml -d train.raw -t valid.raw -o model.out/model.pth > model.out/log.iter 2> model.out/err.iter ``` Test ``` ## you can check the test.raw error deepks test -m model.out/model.pth -d test.raw -o test_corr/test >L1L2.out ## or you can use `get_energy.py` get the output `energy` python get_energy.py --raw you_test.raw ```