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 ## About DeePTB
 
-**DeePTB** is an innovative Python package that employs deep learning to construct electronic Hamiltonians using a minimal basis Slater-Koster TB(**SKTB**), and full LCAO basis using E3-equivariant neural networks (**E3TB**). It is designed to:
+**DeePTB** is an innovative Python package that employs deep learning to construct electronic Hamiltonians using a minimal basis Slater-Koster TB(**DeePTB-SK**), and full LCAO basis using E3-equivariant neural networks for quantum operators including Hamiltonian, overlap, and density matrix (**DeePTB-E3**). It is designed to:
 
 - Efficiently predict TB/LCAO Hamiltonians for large, unseen structures based on training with smaller ones.
+- Efficiently predict LCAO-Density matrix and hence charge density as well as the orbital overlap matrix.
 - Enable simulations of large systems under structural perturbations, finite temperature simulations integrating molecular dynamics (MD) for comprehensive atomic and electronic behavior.
 
-For **SKTB**:
+For **DeePTB-SK**:
 - Support customizable Slater-Koster parameterization with neural network incorporation for local environmental corrections. 
 - Operate independently of the choice of bases and exchange-correlation functionals, offering flexibility and adaptability.
 - Handle systems with strong spin-orbit coupling (SOC) effects.
 
-For **E3TB**:
+For **DeePTB-E3**:
 - Support constructing DFT Hamiltonians/density and overlap matrices under full LCAO basis.
 - Utilize strictly local and semi-local E3-equivariant neural networks to achieve high data-efficiency and accuracy.
 - Speed up via SO(2)convolution to support LCAO basis containing f and g orbitals.
 
-**DeePTB** is a versatile tool adaptable for a wide range of materials and phenomena, providing accurate and efficient simulations. See more details in our DeePTB paper: [sktb: arXiv:2307.04638](http://arxiv.org/abs/2307.04638), [e3tb: arXiv:2407.06053](https://arxiv.org/pdf/2407.06053)
+**DeePTB** is a versatile tool adaptable for a wide range of materials and phenomena, providing accurate and efficient simulations. See more details in our DeePTB paper: [deeptb-sk: arXiv:2307.04638](http://arxiv.org/abs/2307.04638), [deeptb-e3: arXiv:2407.06053](https://arxiv.org/pdf/2407.06053)
 
 
 ## Installation