Geometry Optimization

Overview

PYSEQM provides two approaches for finding minimum-energy structures:

• geomeTRIC integration

  Robust, advanced algorithms using an external package called geomeTRIC; single-molecule only.

• Steepest-Descent (SD) via Geometry_Optimization_SD driver

  Batch-capable but may converge extremely slowly or fail on complex surfaces.

geomeTRIC Integration

PYSEQM can run robust optimization on a single molecule using geomeTRIC as the optimization driver. First install the geomeTRIC interface:

cd PYSEQM/
pip install '.[geomopt]'

Then in your script:

from seqm.geometryOptimization import geomeTRIC_optimization

# `molecules` must be a single-entry batch:
geomeTRIC_optimization(molecules)

Example optimization with geomeTRIC

import torch
from seqm.seqm_functions.constants import Constants
from seqm.Molecule import Molecule
from seqm.geometryOptimization import geomeTRIC_optimization

torch.set_default_dtype(torch.float64)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

species = torch.as_tensor([[6, 6, 6, 6, 6, 6, 1, 1, 1, 1, 1, 1]] ,
                          dtype=torch.int64, device=device)

coordinates = torch.tensor([
                             [
                               [ -35.894866 ,   9.288626 ,  10.054420],
                               [ -36.369609 ,   8.136872 ,   9.487425],
                               [ -34.536331 ,   9.569570 ,   9.955502],
                               [ -35.492308 ,   7.159723 ,   8.983842],
                               [ -33.712643 ,   8.697458 ,   9.304412],
                               [ -34.183340 ,   7.494548 ,   8.762036],
                               [ -36.585465 ,   9.939934 ,  10.537961],
                               [ -37.450824 ,   8.064607 ,   9.436016],
                               [ -34.070057 ,  10.502494 ,  10.500065],
                               [ -35.950748 ,   6.201235 ,   8.765212],
                               [ -33.438373 ,   6.826374 ,   8.246956],
                               [ -32.654911 ,   8.872607 ,   9.123019]
                              ],
                           ], device=device)

const = Constants().to(device)

seqm_parameters = {
                   'method' : 'AM1',
                   'scf_eps' : 1.0e-8,
                   'scf_converger' : [0,0.1],
                   }

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

geomeTRIC_optimization(molecules)

Built-in Steepest-Descent

Use Geometry_Optimization_SD driver for simple, batched optimizations:

from seqm.MolecularDynamics import Geometry_Optimization_SD

opt = Geometry_Optimization_SD(
    seqm_parameters,
    alpha=0.008,          # scaling factor for step size
    force_tol=1e-2,       # converge when max force < 0.01 eV/Å
    max_evl=40            # max steps (energy+force evaluations)
).to(device)

max_force, dE = opt.run(molecule)

• alpha (float): scaling factor for scaling step size of coordinate updates

• force_tol (float): convergence threshold on maximum force

• max_evl (int): maximum number of evaluations

Warning

Steepest-descent can be very inefficient and may fail for rough potential-energy surfaces.