Quickstart

This quickstart is a high-level introduction on how to get started with using PySEQM.

For full examples, please check out our GitHub repository and the examples directory.

Single Point SCF

import torch
from seqm.seqm_functions.constants import Constants
from seqm.Molecule import Molecule
from seqm.ElectronicStructure import Electronic_Structure

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)


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,
   'eig': True
}

molecules = Molecule(const, seqm_parameters, coordinates, species).to(device)
esdriver = Electronic_Structure(seqm_parameters).to(device)
esdriver(molecules)

Molecular Dynamics(NVE)

import torch
from seqm.seqm_functions.constants import Constants
from seqm.Molecule import Molecule
from seqm.MolecularDynamics import Molecular_Dynamics_Basic
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)

const = Constants().to(device)

seqm_parameters = {
   'method': 'AM1',
   'scf_eps': 1.0e-6,
   'scf_converger': [2, 0.0],
   'sp2': [False, 1.0e-5],
   'learned': [],
   'pair_outer_cutoff': 1.0e10,
}

output = {
'molid': [0],
'thermo': 1,
'dump': 1,
'prefix': '../../Outputs_location'
}

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)
_ = md.run(molecule, 10, remove_com=[True, 1], Info_log=True)

Molecular Dynamics(Langevin Thermostat)

Simulates atomic trajectories under the influence of both deterministic interatomic forces and stochastic collisions with an implicit thermal bath. Temperature is controlled by damping and random noise, mimicking a system in thermal equilibrium.

import torch
from seqm.seqm_functions.constants import Constants
from seqm.Molecule import Molecule
from seqm.MolecularDynamics import Molecular_Dynamics_Basic, Molecular_Dynamics_Langevin
from seqm.seqm_functions.read_xyz import read_xyz
import warnings

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)

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,
   'eig': True
}


output = {
'molid': [0,1],
'thermo': 1,
'dump': 1,
'prefix':
'../../Outputs_location'
}

molecule = Molecule(const, seqm_parameters, coordinates, species).to(device)
md = Molecular_Dynamics_Langevin(damp=100.0, seqm_parameters=seqm_parameters, Temp=400.0, timestep=0.4, output=output).to(device)
md.initialize_velocity(molecule)
_ = md.run(molecule, 10, remove_com=[True, 1], Info_log=True)