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DeePaTB: A Deep Learning-Powered Semi-Empirical Quantum Mechanical Method

Install Manual

Requirements

First, you need to install Amesp. You can download it from the official website: https://www.amesp.xyz/download/. We also provide a compatible version of Amesp for your convenience.
We strongly recommend using python==3.9.0 to ensure optimal compatibility. and the datasets in https://zenodo.org/records/19156102

Step 1: Configure Amesp and Python environment

# Add Amesp binary directory to system PATH
export PATH=$PATH:/path/to/amesp/bin/

# Create a dedicated Conda environment with Python 3.9.0
mamba create -n deepatb2 python=3.9.0 -y

# Activate the environment (critical step)
conda activate deepatb2

# Install Python dependencies
pip install -r requirements.txt

# Alternative: Install with Tsinghua PyPI mirror for faster download (China mainland)
pip install -r requirements.txt -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple

Step 2: Install modified DeePKS-kit

cd deepks-kit

cd deepks-kit
python setup.py install

Usage

A complete example job for Qm7BT is provided in the job directory. Follow the steps below to run the workflow:

Step 1: Prepare training datasets

cd job/01_prepare

# Generate Amesp input files (.aip) - charge and spin are set to 0 and 1 for all systems
python 00_xyzaip.py

# Run Amesp calculations - ensure all jobs complete successfully
sh 01_run.sh

# Generate atom.npy file in npydata directory
python 02_xyztoatomnpy.py --dir file

# Generate descriptor (dm_eig) in npydata directory
python 03_get_aTB_decriptor.py --dir file

# Generate energy label file
python 04_get_delta_energy.py

Step 1: Train the model

cd ../02_train  # Navigate to training directory

# Start model training
sh train.sh

# Calculate predicted energies using the trained DeePaTB model
python get_deepatb_energy.py

a easy path to dm_eig but only use new Amesp

! aTB
>ope
deephf on
end
>xyz 0 1
C      -4.602780000000000      2.228670000000000      0.000000000000000
H      -3.532780000000000      2.228670000000000      0.000000000000000
H      -4.959440000000000      1.272170000000000      0.320630000000000
H      -4.959440000000000      2.429240000000000     -0.988670000000000
H      -4.959440000000000      2.984590000000000      0.668030000000000
end

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a ML-SQM method

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