11. Chain Of States Methods
A chain of states (COS) comprises a set of distinct states (images) and is usually spanned between two minima on a potential energy surface (PES).
When properly relaxed, a COS coincides with a minimum energy path (MEP), or is a good approximation to it. Tangents can be defined for every COS image and together they make up a discretized path that describes the reaction/chemical transformation. The tangents are also used to divide the COS gradient into perpendicular and parallel components, w.r.t the tangents. Relaxation (optimization) of the COS is achieved by minimizing the perpendicular component of the gradient, so only the parallel component remains.
pysisyphus offers different COS implementations, namely Nudged Elasic Band (NEB), including its Adaptive and Free-End (and Free-End-Adaptive) modificiations and different flavors of String methods (Growing String Method, GSM, and Simple Zero Temperature String, SZTS). The GSM implementation is also available for with internal coordinates.
11.1. String method
(When discussing String methods the COS 'images' are usually called 'nodes'.)
In the string method the whole COS is periodically (every n-th cycle) reparametrized by fitting a spline through all nodes and redistributing the nodes them along the spline, according to a predefined parametrization. By choosing between different parametrizations equal node spacing or higher resolution around the highest energy image (HEI) can be achieved. The tangents needed for the gradient projection are obtained as first derivatives of the spline.
Reparametrization every \(n\)-th cycle impedes efficient string optimization, and prevents the use of optimizers with some kind of history Conjugate Gradient (CG). The optimizer history is reset after each reparametrization and a simple Steepest Descent (SD) step is done after reparametrization.
11.2. Nudged Elastic Band
No reparametrization takes place in the NEB method. The parallel gradient component along the tangent is projected out and replaced by an artificial spring force. In principle, optimizing NEBs should be easier as there is no reparametrization and more sophisticated optimizers beyond SD can and should be employed.
11.3. Parallelization
Parallel calculation of multiple images is possible using the Dask.distributed package. The easiest way is to include cluster: True in the cos: section (see below). A documented example is found below:
geom:
type: cartesian
fn: lib:ala_dipeptide_iso_b3lyp_631gd_10_images.trj
calc:
type: orca
keywords: hf-3c tightscf
charge: 0
mult: 1
# Setting pal to a number > 1 is still a good idea, even though
# the value is modified internally in parallel COS runs. As some
# follow-up calculations, e.g., TS-optimizations are serial it is
# important to pick a sensible value for 'pal'.
#
# As my notebook has 6 physical cores I set 'pal' to 6.
pal: 6
cos:
type: neb
# Setting only 'cluster: True' without any 'cluster_kwargs' uses all
# avaiable physical cores by default (logical cores, e.g., from
# Hyperthreading are disabled by default).
cluster: True
# The number of avaiable threads can be controlled by setting 'threads_per_worker'
# to an appropriate value. When 'cluster_kwargs' with some arguments is given
# 'cluster' is automatically set to True.
#
# It is probably a good idea to set 'threads_per_worker' to the number of actual
# physical cores to be utilized.
#
# As there are 1+8+1 images in total and only 8 are moving I pick 4
# workers, so all images can be treated in two batches. In the first
# cycle two additional calculations (first and last image) will be done.
cluster_kwargs:
n_workers: 1
threads_per_worker: 4
# pysisyphus uses resources to manage the different tasks, so the appropriate
# number of CPU resources per worker must be given here. This should be the same
# number as used in threads_per_worker.
#
# As mentioned above; just setting cluster: True should be enough in most of the
# cases.
resources:
CPU: 4
# Alternatively, the address to an external scheduler not managed by
# pysisyphus can be provided. If an address is provided, this takes
# precedence and pysisyphus won't create an internal cluster.
# scheduler: 127.0.0.1:39271
opt:
type: lbfgs
align: True
# Production calculations require many more cycles.
max_cycles: 5
Parallelization via Dask should work for most calculators that are executed via the subprocess module as external processes, but probably not calculators like PySCF.
In order to use/watch the nice dashboard provided by dask, please install a recent version of bokeh (python -m pip install bokeh). By default, the dashboard is available under 127.0.0.1:8787 when the cluster is started by pysisyphus.
11.4. General remarks
Converged COS produce good guesses for subsequent TS searches, when the (splined) HEI is determined. In pysisyphus subsequent TS searches are easily started by including tsopt: in the YAML input. Knowledge of the initial and final images of the COS is used to construct a more complete set of internal coordinates for the TS guess and it is less likely that important coordinates are missed. The initial imaginary mode to follow uphill is selected as the one featuring the highest overlap with HEI tangent.
11.5. YAML example(s)
Below you can find an example YAML-input including the most important options that the user may want to modify when running a GSM optimization.
precontr: # Preconditioning of translation & rotation
preopt: # Preoptimize inital and final geometry
cos:
type: gs # Do a growing string
max_nodes: 9 # Total string will have 9 + 2 == 11 images
climb: False # Enable climbing image (CI), usually a good idea.
climb_rms: 0.005 # rms(forces) threshold for enabling CI
climb_lanczos: False # Use tangent obtained from Lanczos algorithm for CI.
climb_lanczos_rms: 0.005 # rms(forces) threshold for enabling Lanczos algorithm.
reparam_check: rms # Criterian for growing new frontier nodes (rms/norm).
perp_thresh: 0.05 # Threshold for growing new frontier nodes.
reparam_every: 2 # Reparametrize every n-th cycle when NOT fully grown.
reparam_every_full: 3 # Reparametrize every n-th cycle when fully grown.
cluster: False # Parallelize COS calculations using Dask cluster
cluster_kwargs: None # Dict; additional arguments for LocalCluster,
# e.g., LocalCluster
scheduler: None # Address to (external) Dask scheduler
opt:
type: string # Optimizer for GrowingString
stop_in_when_full: -1 # Stop string optimization N cycles after fully grown
# Disabled by -1. Usually it's a better idea to
# further converge the string, after is fully grown.
align: False # Disable Kabsch algorithm. Should be True with
# type: cart
scale_step: global # Scale down step as whole (global) or per image
# (per_image)
tsopt:
type: rsprfo # Continue with TS-optimization of highest energy images
# (HEI) using the RS-P-RFO algorithm
do_hess: True # Calculate hessian at optimized TS geometry
trust_max: 0.3
thresh: gau_loose
calc:
type: orca
keywords: "b3lyp 6-31G* rijcosx"
pal: 4
charge: 0
mult: 1
geom:
type: dlc
fn: [first_preopt.xyz, last_preopt.xyz] # Run GrowingString in delocalized internal coordinates
# (preferred).
For NEB optimizations a different optimizer (not type: string) should be used, e.g.,
QuickMin type: qm in the beginning, and type: lbfgs later on. It is not
yet possible to specify two different optimizers that are used in stages, so if is
desired it must be done manually.
# Taken from examples/complex/06_diels_alder...
geom:
type: cart
fn: diels_alder.trj
calc:
type: xtb
charge: 0
mult: 1
pal: 4
preopt:
max_cycles: 5
interpol: # In NEBs the whole path is interpolated beforehand.
type: redund # Possible values: redund|idpp|lst|linear
between: 10 # Interpolate n-geometries between every pair of supplied
# geometries. For two this yields 1 + 10 + 1 == 12 images,
# for three geometries this yields 1 + 10 + 1 + 10 + 1 == 23
# geometries.
cos:
type: neb
climb: False
align_fixed: True # Align the fixed atoms of the initial and final images along the path.
opt:
type: lbfgs
align: True # Align the image of the current step with the image of the previous step
align_factor: 0.9 # If full alignment is not desired, a factor between 0 and 1
# can be specified.
rms_force: 0.01
max_step: 0.04
tsopt:
type: rsirfo
do_hess: True
max_cycles: 75
thresh: gau_tight
hessian_recalc: 7
irc:
type: eulerpc
rms_grad_thresh: 0.0005
endopt:
Further examples for COS optimizations from .yaml input can be found here.
11.6. General advice for COS optimizations
Start from optimized geometries or use the preopt: key in the YAML input.
Consider fixing the initial and final images fix_ends: True
Always use align: True when optimizing a COS in cartesian coordinates to remove translation and rotation. align: True must not be used when running a COS with DLC.
Don't over-converge a COS. It is usually a better idea to converge a COS loosely and use the highest energy image (HEI) as a guess for a subsequent transition state (TS) search.
If possible use a climbing image (CI) climb: True
When running a growing string calculation (type: gs) use stop_in_when_full: [n] in the opt: section with a small integer [n] to stop the COS relaxation after the string is fully grown
11.7. Chain Of States base class
Base class for chain of state methods
- class pysisyphus.cos.ChainOfStates.ChainOfStates(images, fix_first=True, fix_last=True, align_fixed=True, climb=False, climb_rms=0.005, climb_lanczos=False, climb_lanczos_rms=0.005, climb_fixed=False, ts_opt=False, ts_opt_rms=0.0025, energy_min_mix=False, scheduler=None, cluster=False, cluster_kwargs=None, progress=False)[source]
-
- property atoms
- property calculator
- property cart_coords
Return a flat 1d array containing the cartesian coordinates of all images.
- compare_image_rms_forces(ref_rms)[source]
Compare rms(forces) value of an image against a reference value.
Used to decide if we designate an image as climbing image or a TS node.
Only initiate climbing on a sufficiently converged MEP. This can be determined from a supplied threshold for the RMS force (rms_force) or from a multiple of the RMS force convergence threshold (rms_multiple, default).
- property coords
Return a flat 1d array containing the coordinates of all images.
- property coords3d
- property energy
- property forces
- get_full_cycles()[source]
Return array of integers that indexes self.all_true_forces/self.all_cart_coords.
When the ChainOfStates is not yet fully grown this list will be empty. The items of this list can be used to index self.all_true_forces and related lists, to extract image coordinate & forces data for all cycles when the COS was already fully grown.
This data can then be used to, e.g., construct a (TS)-Hessian for an selected image.
- Return type:
ndarray
- get_tangent(i, kind='upwinding', lanczos_guess=None, disable_lanczos=False)[source]
[1] Equations (8) - (11)
- property gradient
- property image_coords
- property image_inds
- property is_analytical_2d
- property last_index
- logger = <Logger cos (DEBUG)>
- property masses_rep
- property max_image_num
- property moving_images
- property moving_indices
Returns the indices of the images that aren't fixed and can be optimized.
- property nimages: int
- property perpendicular_forces
- prepare_opt_cycle(last_coords, last_energies, last_forces)[source]
Implements additional logic in preparation of the next optimization cycle.
Should be called by the optimizer at the beginning of a new optimization cycle. Can be used to implement additional logic as needed for AdaptiveNEB etc.
- propagate(force_unique_overlap_data_fns=True)[source]
Propagate chkfiles and root information along COS.
Does an energy calculation at every image and tries to propagate the converged wavefunction to the next image.
When excited states should be tracked it is assumed that the correct root is set at the first image. In most cases, e.g. in COS calculations started from YAML input the initial root will be the same for all images. When the correct root switches between the first image (with the correct root) and the last image, then using the same root for all images will be wrong. This is also corrected here. From the second image on the excited state (ES) overlaps are calculated between the current image and the image before it and the root with the highest overlap to the root on the previous image is set on the current image. As we start from the correct root at the first image this will ensure that the correct root is selected at all images.
If requested, unique names for the dumped overlap data HDF5 will be picked.
- property results
- set_coords_at(i, coords)[source]
Called from helpers.procrustes with cartesian coordinates. Then tries to set cartesian coordinate as self.images[i].coords which will raise an error when coord_type != "cart".
- set_zero_forces_for_fixed_images()[source]
This is always done in cartesian coordinates, independent of the actual coord_type of the images as setting forces only work with cartesian forces.
- property use_dask
- valid_coord_types = ('cart', 'cartesian', 'dlc')
- zero_vec
It was a rather unfortunate choice to fill/grow self.coords_list and self.all_true_forces in different methods. self.prepare_opt_cycle() appends to self.coords_list, while self.calculate_forces() appends to self.all_true_forces().
After a succsessful COS optimization both lists differ in length; self.all_true_forces has 1 additional item, compared to self.coords_list, as self.calculate_forces() is called in Optimizer.prepare_opt() once. Afterwards, self.coords_list and self.all_true_forces grow in a consistent manner.
Two choices can be made: keep this discrepancy in mind and omit/neglect the first item in self.coords_list, or grow another list in self.calculate_forces(). For now, we will go with the latter option.
11.8. Chain Of State Methods
11.8.1. Nudged Elastic Band (NEB)
- class pysisyphus.cos.NEB.NEB(images, variable_springs=False, k_max=0.3, k_min=0.1, perp_spring_forces=None, bandwidth=None, **kwargs)[source]
Bases:
ChainOfStates- property forces
- property parallel_forces
11.8.2. Adaptive NEB
- class pysisyphus.cos.AdaptiveNEB.AdaptiveNEB(images, adapt=True, adapt_fact=0.25, adapt_between=1, scale_fact=False, keep_hei=True, free_ends=True, **kwargs)[source]
Bases:
NEB- __init__(images, adapt=True, adapt_fact=0.25, adapt_between=1, scale_fact=False, keep_hei=True, free_ends=True, **kwargs)[source]
(Free-End) Adaptive Nudged Elastic Band.
- Parameters:
images (list of Geometry objects) -- Images of the band.
adapt (bool, default True) -- Whether to adapt the image number or not. This switch is included to support the FreeEndNEB class, that is just a thin wrapper around this class.
adapt_fact (positive float) -- Factor that is used to decide wether to adapt. The inital threshold is calculated by multiplying the RMS force of the band with this factor. When the RMS of the force falls below this threshold adaption takes place.
adapat_between (positive integer) -- Number of images to interpolate between the highest energy image and its neighbours. The number of starting images must be higher or equal then 2*adapt_between+3, as we reuse/transfer the calculators from the starting images onto the new ones.
scale_fact (bool, default False) -- Whether to increase adapt_fact in deeper levels. This may lead to earlier adapation.
keep_hei (bool, optional) -- Whether to keep the highest energy image (usually a very good idea) or to interpolate only between the neighbouring images.
free_ends (bool, default True) -- Whether to use modified forces on the end images.
- adapt_this_cycle(forces)[source]
Decide wether to adapt.
- Parameters:
forces (np.array) -- Forces of the previous optimization cycle.
- Returns:
adapt -- Flag that indicates if adaption should take place in this cycle.
- Return type:
bool
- property forces
See Eq. (7) in [2].
11.8.3. Free-End NEB
- class pysisyphus.cos.FreeEndNEB.FreeEndNEB(*args, fix_first=False, fix_last=False, **kwargs)[source]
Bases:
AdaptiveNEB- __init__(*args, fix_first=False, fix_last=False, **kwargs)[source]
Simple Free-End-NEB method.
Derived from AdaptiveNEB with disabled adaptation. Only implements Eq. (7) from [2]. For other implementations please see the commit 01bc8812ca6f1cd3645d43e0337d9e3c5fb0ba55. There the other variants are present but I think Eq. (7) in [2] is the simplest & best bet.
11.8.4. Simple Zero-Temperature String
- class pysisyphus.cos.SimpleZTS.SimpleZTS(images, param='equal', **kwargs)[source]
Bases:
ChainOfStates
11.9. Growing Chain Of States base class
Base class for growing chain of state methods
- class pysisyphus.cos.GrowingChainOfStates.GrowingChainOfStates(images, calc_getter, max_nodes=10, **kwargs)[source]
- property arc_dims
- property max_image_num
- prepare_opt_cycle(*args, **kwargs)[source]
Implements additional logic in preparation of the next optimization cycle.
Should be called by the optimizer at the beginning of a new optimization cycle. Can be used to implement additional logic as needed for AdaptiveNEB etc.
- propagate(force_unique_overlap_data_fns=True)[source]
Propagate chkfiles and root information along COS.
Does an energy calculation at every image and tries to propagate the converged wavefunction to the next image.
When excited states should be tracked it is assumed that the correct root is set at the first image. In most cases, e.g. in COS calculations started from YAML input the initial root will be the same for all images. When the correct root switches between the first image (with the correct root) and the last image, then using the same root for all images will be wrong. This is also corrected here. From the second image on the excited state (ES) overlaps are calculated between the current image and the image before it and the root with the highest overlap to the root on the previous image is set on the current image. As we start from the correct root at the first image this will ensure that the correct root is selected at all images.
If requested, unique names for the dumped overlap data HDF5 will be picked.
11.10. Growing Chain Of State Methods
11.10.1. Growing String Method
- class pysisyphus.cos.GrowingString.GrowingString(images, calc_getter, perp_thresh=0.05, param='equi', reparam_every=2, reparam_every_full=3, reparam_tol=None, reparam_check='rms', max_micro_cycles=5, reset_dlc=True, climb=False, **kwargs)[source]
Bases:
GrowingChainOfStates- property forces
- property full_string_image_inds
- property fully_grown
Returns wether the string is fully grown. Don't count the first and last node.
- get_new_image(ref_index)[source]
Get new image by taking a step from self.images[ref_index] towards the center of the string.
- property image_inds
- property left_size
- property lf_ind
Index of the left frontier node in self.images.
- property nodes_missing
Returns the number of nodes to be grown.
- property rf_ind
Index of the right frontier node in self.images.
- property right_size
- property string_size