ASE Manual Release 3.6.1.2825 CAMd - CampOS Wiki

ASE Manual, Release 3.6.1.2828
In all three filters only the hydrogen atoms are made visible. When asking for the positions only the positions of
the hydrogen atoms are returned.
7.16 Nudged elastic band
The Nudged Elastic Band method is a technique for finding transition paths (and corresponding energy barriers)
between given initial and final states. The method involves constructing a “chain” of “replicas” or “images” of the
system and relaxing them in a certain way.
Relevant literature References:
1. H. Jonsson, G. Mills, and K. W. Jacobsen, in ‘Classical and Quantum Dynamics in Condensed Phase Systems’,
edited by B. J. Berne, G. Cicotti, and D. F. Coker, World Scientific, 1998 [standard formulation]
2. ‘Improved Tangent Estimate in the NEB method for Finding Minimum Energy Paths and Saddle Points’,
Graeme Henkelman and Hannes Jonsson, J. Chem. Phys. 113, 9978 (2000) [improved tangent estimates]
3. ‘A Climbing-Image NEB Method for Finding Saddle Points and Minimum Energy Paths’, Graeme Henkelman,
Blas P. Uberuaga and Hannes Jonsson, J. Chem. Phys. 113, 9901 (2000)
7.16.1 The NEB class
This module defines one class:
class ase.neb.NEB(images, k=0.1, climb=False, parallel=False, world=None)
Nudged elastic band.
images: list of Atoms objects Images defining path from initial to final state.
k: float or list of floats Spring constant(s). One number or one for each spring.
climb: bool Use a climbing image (default is no climbing image).
parallel: bool Distribute images over processors.
Example of use, between initial and final state which have been previously saved in A.traj and B.traj:
from ase import io
from ase.neb import NEB
from ase.optimize import MDMin
# Read initial and final states:
initial = io.read(’A.traj’)
final = io.read(’B.traj’)
# Make a band consisting of 5 images:
images = [initial]
images += [initial.copy() for i in range(3)]
images += [final]
neb = NEB(images)
# Interpolate linearly the potisions of the three middle images:
neb.interpolate()
# Set calculators:
for image in images[1:4]:
image.set_calculator(MyCalculator(...))
# Optimize:
optimizer = MDMin(neb, trajectory=’A2B.traj’)
optimizer.run(fmax=0.04)
Be sure to use the copy method (or similar) to create new instances of atoms within the list of images fed to the
NEB. Do not use something like [initial for i in range(3)], as it will only create references to the original atoms
object.
Notice the use of the interpolate() method to get a good initial guess for the path from A to B.
7.16. Nudged elastic band 143