fix docs merge conflicts

Author: Vishikh Athavale

docs/source/index.rst

@@ -1,14 +1,10 @@
 Welcome to PYSEQM Documentation
 ==================================
 
-PYSEQM(PYtorch-based Semi-Empirical Quantum Mechanics) is a Semi-Empirical Quantum Mechanics package implemented in PyTorch. It provides built-in interfaces for machine learning and efficient molecular dynamic engines with GPU support. 
+PYSEQM (PYtorch-based Semi-Empirical Quantum Mechanics) is a Semi-Empirical Quantum Mechanics package implemented in PyTorch. It provides built-in interfaces for machine learning and efficient molecular dynamic engines with GPU support. 
 
 
-<<<<<<< HEAD
-Several molecular dynamics algorithms are implemented for facilitating dynamics simulations, inlcuding orginal and Extended Lagrangian Born-Oppenheimer Molecular Dynamics, geometry optimization and thermostats.
-=======
-Several molecular dynamics algorithms are implemented for facilitating dynamic simulations, inlcuding Extended Lagrangian Born-Oppenheimer Molecular Dynamics, geometric optimization and several thermostats.
->>>>>>> b817174 (update)
+Several molecular dynamics algorithms are implemented for facilitating dynamics simulations, including Born-Oppenheimer Molecular Dynamics (BOMD), Extended-Lagrangin BOMD, geometry optimization and thermostats.
 
 
 

docs/source/quick_start.rst

@@ -4,10 +4,7 @@ Quickstart
 
 
 
-<<<<<<< HEAD
 This quickstart is a high-level introduction on how to get started with using PySEQM. 
-=======
-This quickstart is a high level introduction on how to get started using PySEQM. 
 
 For full examples, please check out our `GitHub repository <https://github.com/lanl/pyseqm>`_ and the `examples directory <https://github.com/lanl/PYSEQM/tree/master/examples>`_.
 
@@ -19,9 +16,6 @@ For full examples, please check out our `GitHub repository <https://github.com/l
 Single Point SCF
 ------------------------------
 
-
-Computes the electronic structure of a molecule by iteratively solving for the electron density or wavefunction until convergence, enabling calculation of total energy and related properties.
-
 .. code-block:: python
 
 
@@ -77,140 +71,11 @@ Computes the electronic structure of a molecule by iteratively solving for the e
    molecules = Molecule(const, seqm_parameters, coordinates, species).to(device)
    esdriver = Electronic_Structure(seqm_parameters).to(device)
    esdriver(molecules)
->>>>>>> b817174 (update)
-
-
-
-
-
-
-SCF and Excited State Calculations
-------------------------------
-
-<<<<<<< HEAD
-Once initialized, an SCF calculation can be run directly to compute total energy:
-=======
-Estimates the energies of electronically excited states by solving for higher energy eigenvalues of the electronic Hamiltonian, extending SCF results beyond the ground state.
->>>>>>> b817174 (update)
-
-.. code-block:: python
-
-
-
-   import torch
-   from seqm.seqm_functions.constants import Constants
-   from seqm.Molecule import Molecule
-   from seqm.ElectronicStructure import Electronic_Structure
-   from seqm.seqm_functions.read_xyz import read_xyz
-
-
-   torch.set_default_dtype(torch.float64)
-   device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-
-
-   species = torch.as_tensor([[8,6,1,1],
-                              [8,6,1,1],
-                              [8,8,6,0]],
-                           dtype=torch.int64, device=device)
-
-   coordinates = torch.tensor([
-                                 [
-                                 [0.00,    0.00,    0.00],
-                                 [1.22,    0.00,    0.00],
-                                 [1.82,    0.94,    0.00],
-                                 [1.82,   -0.94,    0.00]
-                                 ],
-                                 [
-                                 [0.00,    0.00,    0.00],
-                                 [1.22,    0.00,    0.00],
-                                 [1.82,    0.94,    0.00],
-                                 [1.82,   -0.94,    0.00]
-                                 ],
-                                 [
-                                 [0.00,    0.00,    0.00],
-                                 [1.23,    0.00,    0.00],
-                                 [1.82,    0.94,    0.00],
-                                 [0.0,     0.0,     0.0]
-                                 ]
-                              ], device=device)
-
-<<<<<<< HEAD
-.. code-block:: python
-
-
-
-
-   species, coordinates = read_xyz(['./data.xyz'])
-=======
->>>>>>> b817174 (update)
-
-   species = torch.as_tensor(species, dtype=torch.int64, device=device)[:]
-   coordinates = torch.tensor(coordinates, device=device)[:]
-   const = Constants().to(device)
-
-   elements = [0] + sorted(set(species.reshape(-1).tolist()))
-
-   seqm_parameters = {
-      'method': 'AM1',
-      'scf_eps': 1.0e-8,
-      'scf_converger': [2, 0.0],
-      'sp2': [False, 1.0e-5],
-      'elements': elements,
-      'learned': [],
-      'pair_outer_cutoff': 1.0e8,
-      'eig': True,
-      'excited_states': {'n_states': 10},
-   }
-
-<<<<<<< HEAD
-User-defined parameters for calculations are set using the seqm_parameters dictionary.
-
-
-**method:** Austin Model 1
-
-**scf_eps:** If the energy change between two SCF steps is smaller than this value, then SCF is converged.
-
-**scf_converger:** Converger used for SCF.
-
-**sp2:** 
-
-**elements:** Atomic numbers in the data from species.
-
-**learned:** Learned parameters name list.
-
-**pair_outer_cutoff:** The value for how far apart two atoms can be before their interaction is ignored.
-
-**eig:** Whether or not to diagonalize the Fock matrix.
-
-**excited_states:** The number of excited states to calcuate.
-
-
-
-
-
-.. code-block:: python
-
-
-
-=======
->>>>>>> b817174 (update)
-   molecules = Molecule(const, seqm_parameters, coordinates, species).to(device)
-   esdriver = Electronic_Structure(seqm_parameters).to(device)
-   esdriver(molecules)
-
 
 
 Molecular Dynamics(NVE)
 ----------------------
 
-<<<<<<< HEAD
-You can run molecular dynamics using Born-Oppenheimer Molecular Dynamics (BOMD) or Extended-Lagrangian BOMD:
-=======
-Tracks the natural evolution of a system of atoms under Newton’s laws in an isolated environment—no energy exchange with surroundings. Energy is conserved, and atomic motion arises solely from interatomic forces.
-
->>>>>>> b817174 (update)
-
 .. code-block:: python
 
    import torch
@@ -222,15 +87,6 @@ Tracks the natural evolution of a system of atoms under Newton’s laws in an is
    torch.set_default_dtype(torch.float64)
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-
-<<<<<<< HEAD
-Add import statements for PyTorch, and the relevant seqm modules.
-Set the datatype to float64 for calculations in double precision and set device to GPU.
-
-.. code-block:: python
-
-   species, coordinates = read_xyz(['./data.xyz'])
-=======
    species = torch.as_tensor([[8,6,1,1],
                               [8,6,1,1],
                               [8,8,6,0]],
@@ -256,54 +112,25 @@ Set the datatype to float64 for calculations in double precision and set device
                                  [0.0,     0.0,     0.0]
                                  ]
                               ], device=device)
->>>>>>> b817174 (update)
 
-   species = torch.as_tensor(species, dtype=torch.int64, device=device)[:]
-   coordinates = torch.tensor(coordinates, device=device)[:]
    const = Constants().to(device)
 
-   elements = [0] + sorted(set(species.reshape(-1).tolist()))
-
    seqm_parameters = {
       'method': 'AM1',
       'scf_eps': 1.0e-6,
       'scf_converger': [2, 0.0],
       'sp2': [False, 1.0e-5],
-      'elements': elements,
       'learned': [],
       'pair_outer_cutoff': 1.0e10,
    }
 
-
-<<<<<<< HEAD
-User-defined parameters for calculations are set using the seqm_parameters dictionary.
-
-.. code-block:: python
-
-
-
-=======
->>>>>>> b817174 (update)
    output = {
    'molid': [0], 
    'thermo': 1, 
    'dump': 1, 
    'prefix': '../../Outputs_location'
    }
 
-<<<<<<< HEAD
-The 'molid' key takes a list as the value. This list should contain the indices of the molecules on which MD has to be run, if multiple molecules have been given as input.
-
-xxx
-
-xxx
-
-Set the output file path.
-
-.. code-block:: python
-
-=======
->>>>>>> b817174 (update)
    molecule = Molecule(const, seqm_parameters, coordinates, species).to(device)
    md = Molecular_Dynamics_Basic(seqm_parameters=seqm_parameters, Temp=300.0, timestep=0.4, output=output).to(device)
    md.initialize_velocity(molecule)
@@ -385,4 +212,3 @@ Simulates atomic trajectories under the influence of both deterministic interato
    md.initialize_velocity(molecule)
    _ = md.run(molecule, 10, remove_com=[True, 1], Info_log=True)
 
-