prepare for PR

Author: JGHan7

README.md

@@ -12,7 +12,9 @@
 
 # About ABACUS
 
-ABACUS (Atomic-orbital Based Ab-initio Computation at UStc) is an open-source package based on density functional theory (DFT). The package utilizes both plane wave and numerical atomic basis sets with the usage of norm-conserving pseudopotentials to describe the interactions between nuclear ions and valence electrons. ABACUS supports LDA, GGA, meta-GGA, and hybrid functionals. Apart from single-point calculations, the package allows geometry optimizations and ab-initio molecular dynamics with various ensembles. The package also provides a variety of advanced functionalities for simulating materials, including the DFT+U, VdW corrections, and implicit solvation model, etc. In addition, ABACUS strives to provide a general infrastructure to facilitate the developments and applications of novel machine-learning-assisted DFT methods (DeePKS, DP-GEN, DeepH, etc.) in molecular and material simulations.
+ABACUS (Atomic-orbital Based Ab-initio Computation at UStc) is an open-source package based on density functional theory (DFT). The package utilizes both plane wave and numerical atomic basis sets with the usage of norm-conserving pseudopotentials to describe the interactions between nuclear ions and valence electrons. ABACUS supports LDA, GGA, meta-GGA, and hybrid functionals. Apart from single-point calculations, the package allows geometry optimizations and ab-initio molecular dynamics with various ensembles. The package also provides a variety of advanced functionalities for simulating materials, including the DFT+U, VdW corrections, and implicit solvation model, etc. In addition, ABACUS strives to provide a general infrastructure to facilitate the developments and applications of novel machine-learning-assisted DFT methods (DeePKS, DP-GEN, DeepH, DeePTB etc.) in molecular and material simulations.
 
 # Online Documentation
 For detailed documentation, please refer to [our documentation website](https://abacus.deepmodeling.com/).
+
+See our [Github Pages](https://mcresearch.github.io/abacus-user-guide/) for more tutorials and developer guides.

docs/CITATIONS.md

@@ -26,7 +26,7 @@ The following references are required to be cited when using ABACUS. Specificall
 
 - **If DeePKS is used:**
 
-    Wenfei Li, Qi Ou, et al. "DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials." <https://arxiv.org/abs/2206.10093>.
+    Wenfei Li, Qi Ou, et al. "DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials." J. Phys. Chem. A 126.49 (2022): 9154-9164.
 
 - **If hybrid functional is used:**
 

docs/advanced/input_files/input-main.md

@@ -428,7 +428,7 @@
     - [ri\_hartree\_benchmark](#ri_hartree_benchmark)
     - [aims_nbasis](#aims_nbasis)
   - [Reduced Density Matrix Functional Theory](#Reduced-Density-Matrix-Functional-Theory)
-    - [ab\_initio\_type](#ab_initio_type)
+    - [rdmft](#rdmft)
     - [rdmft\_power\_alpha](#rdmft_power_alpha)
 
 [back to top](#full-list-of-input-keywords)
@@ -731,7 +731,7 @@ These variables are used to control the plane wave related parameters.
 
 - **Type**: Real
 - **Description**: Energy cutoff for plane wave functions, the unit is **Rydberg**. Note that even for localized orbitals basis, you still need to setup an energy cutoff for this system. Because our local pseudopotential parts and the related force are calculated from plane wave basis set, etc. Also, because our orbitals are generated by matching localized orbitals to a chosen set of wave functions from a certain energy cutoff, this set of localize orbitals is most accurate under this same plane wave energy cutoff.
-- **Default**: 50
+- **Default**: 50 Ry (PW basis), 100 Ry (LCAO basis)
 
 ### ecutrho
 
@@ -3928,7 +3928,7 @@ Currently supported: `RPA`, `LDA`, `PBE`, `HSE`, `HF`.
 
 - **Type**: String
 - **Description**: The method to solve the Casida equation $AX=\Omega X$ in LR-TDDFT under Tamm-Dancoff approximation (TDA), where $A_{ai,bj}=(\epsilon_a-\epsilon_i)\delta_{ij}\delta_{ab}+(ai|f_{Hxc}|bj)+\alpha_{EX}(ab|ij)$ is the particle-hole excitation matrix and $X$ is the transition amplitude.
-  - `dav`: Construct $AX$ and diagonalize the Hamiltonian matrix iteratively with Davidson algorithm.
+  - `dav`/`dav_subspace`/ `cg`: Construct $AX$ and diagonalize the Hamiltonian matrix iteratively with Davidson/Non-ortho-Davidson/CG algorithm.
   - `lapack`: Construct the full $A$ matrix and directly diagonalize with LAPACK.
   - `spectrum`: Calculate absorption spectrum only without solving Casida equation. The `OUT.${suffix}/` directory should contain the
   files for LR-TDDFT eigenstates and eigenvalues, i.e. `Excitation_Energy.dat` and `Excitation_Amplitude_${processor_rank}.dat`
@@ -4007,11 +4007,11 @@ The output files are `OUT.${suffix}/Excitation_Energy.dat` and `OUT.${suffix}/Ex
 ab-initio methods and the xc-functional parameters used in RDMFT.
 The physical quantities that RDMFT temporarily expects to output are the kinetic energy, total energy, and 1-RDM of the system in the ground state, etc.
 
-### ab_initio_type
+### rdmft
 
-- **Type**: String
-- **Description**: Determine the ab-initio method, ksdft or rdmft (reduced density matrix funcional theory)
-- **Default**: ksdft
+- **Type**: Boolean
+- **Description**: Whether to perform rdmft calculation (reduced density matrix funcional theory)
+- **Default**: false
 
 ### rdmft_power_alpha
 

docs/advanced/interface/deepks.md

@@ -1,9 +1,14 @@
 # DeePKS
 
-[DeePKS](https://pubs.acs.org/doi/10.1021/acs.jctc.0c00872) is a machine-learning aided density funcitonal model that fits the energy difference between highly accurate but computationally demanding method and effcient but less accurate method via neural-network. As such, the trained DeePKS model can provide highly accurate energetics (and forces) with relatively low computational cost, and can therefore act as a bridge to connect expensive quantum mechanic data and machine-learning-based potentials. While the original framework of DeePKS is for molecular systems, please refer to this [reference](https://arxiv.org/abs/2206.10093) for the application of DeePKS in periodic systems. 
+[DeePKS](https://pubs.acs.org/doi/10.1021/acs.jctc.0c00872) is a machine-learning (ML) aided density funcitonal model that fits the energy difference between highly accurate but computationally demanding method and effcient but less accurate method via neural-network. Common high-precision methods include hybrid functionals or CCSD-T, while common low-precision methods are LDA/GGA.
 
-Detailed instructions on installing and running DeePKS can be found on this [website](https://deepks-kit.readthedocs.io/en/latest/index.html). An [example](https://github.com/deepmodeling/deepks-kit/tree/abacus/examples/water_single_lda2pbe_abacus) for training DeePKS model with ABACUS is also provided. The DeePKS-related keywords in `INPUT` file can be found [here](http://abacus.deepmodeling.com/en/latest/advanced/input_files/input-main.html#deepks).
+As such, the trained DeePKS model can provide highly accurate energetics (and forces/band gap/density) with relatively low computational cost, and can therefore act as a bridge to connect expensive quantum mechanic data and machine-learning-based potentials. 
+While the original framework of DeePKS is for molecular systems, please refer to this [J. Phys. Chem. A 126.49 (2022): 9154-9164](https://pubs.acs.org/doi/abs/10.1021/acs.jpca.2c05000) for the application of DeePKS in periodic systems.
 
-> Note: Use the LCAO basis for DeePKS-related calculations
+Detailed instructions on installing and running DeePKS can be found on this [website](https://deepks-kit.readthedocs.io/en/latest/index.html). The DeePKS-related keywords in `INPUT` file can be found [here](http://abacus.deepmodeling.com/en/latest/advanced/input_files/input-main.html#deepks). An [example](https://github.com/deepmodeling/deepks-kit/tree/abacus/examples/water_single_lda2pbe_abacus) for training DeePKS model with ABACUS is also provided. For practical applications, users can refer to a series of [Notebooks](https://bohrium.dp.tech/collections/1921409690). These Notebooks provide detailed instructions on how to train and use the DeePKS model using perovskite as an example. Currently, these tutorials are available in Chinese, but we plan to release corresponding English versions in the near future. 
+
+
+
+> Note: DeePKS calculations can only be performed by the LCAO basis.
 
 

docs/advanced/interface/deeptb.md

@@ -0,0 +1,5 @@
+# DeePTB
+
+[DeePTB](https://github.com/deepmodeling/DeePTB) is an innovative Python package that uses deep learning to accelerate ab initio electronic structure simulations. It offers versatile, accurate, and efficient simulations for a wide range of materials and phenomena. Trained on small systems, DeePTB can predict electronic structures of large systems, handle structural perturbations, and integrate with molecular dynamics for finite temperature simulations, providing comprehensive insights into atomic and electronic behavior. See more details in [DeePTB-SK: Nat Commun 15, 6772 (2024)](https://www.nature.com/articles/s41467-024-51006-4) and [DeePTB-E3: arXiv:2407.06053](https://arxiv.org/pdf/2407.06053).
+
+DeePTB trains the model based on the Structure, Eigenvalues, Hamiltonian, Density matrix, and Overlap matrix  from first-principles calcualtions. DeePTB team provides the interfaces [dftio](https://github.com/deepmodeling/dftio) with other first-principles softwares. [dftio](https://github.com/deepmodeling/dftio) fully supports the interfaces with ABACUS, and can transfer the Structure, Eigenvalues, Hamiltonian, Density matrix, and Overlap matrix from ABACUS into the format used in [DeePTB](https://github.com/deepmodeling/DeePTB).

docs/advanced/interface/index.rst

@@ -9,6 +9,7 @@ Interfaces to Other Softwares
    deepks
    dpgen
    deeph
+   deeptb
    Hefei-NAMD
    phonopy
    Wannier90

docs/conf.py

@@ -33,7 +33,6 @@
 extensions = [
         'myst_parser',
         'deepmodeling_sphinx',
-        'sphinxcontrib.jquery',
 ]
 myst_enable_extensions = [
     "amsmath",
@@ -66,7 +65,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'sphinx_rtd_theme'
+html_theme = 'sphinx_book_theme'
 html_logo = 'abacus-logo.svg'
 
 

docs/requirements.txt

@@ -1,4 +1,4 @@
 urllib3
 myst_parser[linkify]
-sphinx_rtd_theme
-deepmodeling_sphinx
+sphinx-book-theme
+deepmodeling-sphinx>=0.3.0

python/pyabacus/examples/diago_matrix.py

@@ -10,10 +10,13 @@ def load_mat(mat_file):
     return h_mat, nbasis, nband
 
 def calc_eig_pyabacus(mat_file, method):
-    algo = {
+    dav = {
         'dav_subspace': hsolver.dav_subspace,
         'davidson': hsolver.davidson
     }
+    cg = {
+        'cg': hsolver.cg
+    }
 
     h_mat, nbasis, nband = load_mat(mat_file)
 
@@ -22,25 +25,27 @@ def calc_eig_pyabacus(mat_file, method):
     diag_elem = np.where(np.abs(diag_elem) < 1e-8, 1e-8, diag_elem)
     precond = 1.0 / np.abs(diag_elem)
 
-    def mm_op(x):
+    def mvv_op(x):
         return h_mat.dot(x)
 
-    e, _ = algo[method](
-        mm_op,
-        v0,
-        nbasis,
-        nband,
-        precond,
-        dav_ndim=8,
-        tol=1e-8,
-        max_iter=1000
-    )
+    if method in dav:
+        algo = dav[method]
+        # args: mvvop, init_v, dim, num_eigs, precondition, dav_ndim, tol, max_iter
+        args = (mvv_op, v0, nbasis, nband, precond, 8, 1e-12, 5000)
+    elif method in cg:
+        algo = cg[method]
+        # args: mvvop, init_v, dim, num_eigs, precondition, tol, max_iter
+        args = (mvv_op, v0, nbasis, nband, precond, 1e-12, 5000)
+    else:
+        raise ValueError(f"Method {method} not available")
+    
+    e, _ = algo(*args)
 
     print(f'eigenvalues calculated by pyabacus-{method} is: \n', e)
 
     return e
 
-def calc_eig_scipy(mat_file):
+def calc_eigsh(mat_file):
     h_mat, _, nband = load_mat(mat_file)
     e, _ = scipy.sparse.linalg.eigsh(h_mat, k=nband, which='SA', maxiter=1000)
     e = np.sort(e)
@@ -50,13 +55,12 @@ def calc_eig_scipy(mat_file):
 
 if __name__ == '__main__':
     mat_file = './Si2.mat'
-    method = ['dav_subspace', 'davidson']
+    method = ['dav_subspace', 'davidson', 'cg']
 
     for m in method:
         print(f'\n====== Calculating eigenvalues using {m} method... ======')
         e_pyabacus = calc_eig_pyabacus(mat_file, m)
-        e_scipy = calc_eig_scipy(mat_file)
+        e_scipy = calc_eigsh(mat_file)
 
         print('eigenvalues difference: \n', e_pyabacus - e_scipy)
-    
-    
+        

python/pyabacus/src/hsolver/CMakeLists.txt

@@ -2,6 +2,7 @@
 list(APPEND _diago
     ${HSOLVER_PATH}/diago_dav_subspace.cpp
     ${HSOLVER_PATH}/diago_david.cpp
+    ${HSOLVER_PATH}/diago_cg.cpp
     ${HSOLVER_PATH}/diag_const_nums.cpp
     ${HSOLVER_PATH}/diago_iter_assist.cpp