A first kMC Model–the API way

In general there are two interfaces to defining a new model: A GUI and an API. While the GUI can be quite nice especially for beginners, it turns out that the API is better maintained simply because … well, maintaing a GUI is a lot more work.

So we will start by learning how to setup the model using the API which will turn out not to be hard at all. It is knowing howto do this will also pay-off especially if you starting tinkering with your existing models and make little changes here and there.

Build the model

You may also look at MyFirstDiffusion__build.py in the examples directory.

We start by making the necessary import statements (in *python* or better *ipython*):

import kmcos
from kmcos.types import *
from kmcos.io import *
import numpy as np

which imports all classes that make up a kMC project. The functions from kmcos.io will only be needed at the end to save the project or to export compilable code.

The example sketched out here leads you to a kMC model for CO adsorption and desorption on Pd(100). First you should instantiate a new project and fill in meta information

kmc_model = kmcos.create_kmc_model()
kmc_model.set_meta(author = 'Your Name',
            email = 'your.name@server.com',
            model_name = 'MyFirstModel',
            model_dimension = 2,)

Next you add some species or states. Note that whichever species you add first is the default species with which all sites in the system will be initialized. Of course this can be changed later

For surface science simulations it is useful to define an empty state, so we add

kmc_model.add_species(name='empty')

and some surface species. Given you want to simulate CO adsorption and desorption on a single crystal surface you would say

kmc_model.add_species(name='CO',
               representation="Atoms('CO',[[0,0,0],[0,0,1.2]])")

where the string passed as representation is a string representing a CO molecule which can be evaluated in ASE namespace.

Once you have all species declared is a good time to think about the geometry. To keep it simple we will stick with a simple-cubic lattice in 2D which could for example represent the (100) surface of a fcc crystal with only one adsorption site per unit cell. You start by giving your layer a name

layer = kmc_model.add_layer(name='simple_cubic')

and adding a site

layer.sites.append(Site(name='hollow', pos='0.5 0.5 0.5',
                        default_species='empty'))

Where pos is given in fractional coordinates, so this site will be in the center of the unit cell.

Simple, huh? Now you wonder where all the rest of the geometry went? For a simple reason: the geometric location of a site is meaningless from a kMC point of view. In order to solve the master equation none of the numerical coordinates of any lattice sites matter since the master equation is only defined in terms of states and transition between these. However to allow a graphical representation of the simulation one can add geometry as you have already done for the site. You set the size of the unit cell via

kmc_model.lattice.cell = np.diag([3.5, 3.5, 10])

which are prototypical dimensions for a single-crystal surface in Angstrom.

Ok, let us see what we managed so far: you have a lattice with a site that can be either empty or occupied with CO.

Populate process list and parameter list

The remaining work is to populate the process list and the parameter list. The parameter list defines the parameters that can be used in the expressions of the rate constants. In principle one could do without the parameter list and simply hard code all parameters in the process list, however one looses some nifty functionality like easily changing parameters on-the-fly or even interactively.

A second benefit is that you achieve a clear separation of the kinetic model from the barrier input, which usually has a different origin.

In practice filling the parameter list and the process list is often an iterative process, however since we have a fairly short list, we can try to set all parameters at once.

First of all you want to define the external parameters to which our model is coupled. Here we use the temperature and the CO partial pressure:

kmc_model.add_parameter(name='T', value=600., adjustable=True, min=400, max=800)
kmc_model.add_parameter(name='p_CO', value=1., adjustable=True, min=1e-10, max=1.e2)

You can also set a default value and a minimum and maximum value set defines how the scrollbars a behave later in the runtime GUI.

To describe the adsorption rate constant you will need the area of the unit cell:

kmc_model.add_parameter(name='A', value='(3.5*angstrom)**2')

Last but not least you need a binding energy of the particle on the surface. Since without further ado we have no value for the gas phase chemical potential, we’ll just call it deltaG and keep it adjustable

kmc_model.add_parameter(name='deltaG', value='-0.5', adjustable=True,
                           min=-1.3, max=0.3)

To define processes we first need a coordinate [2]

coord = kmc_model.lattice.generate_coord('hollow.(0,0,0).simple_cubic')

Then you need to have at least two processes. A process or elementary step in kMC means that a certain local configuration must be given so that something can happen at a certain rate constant. In the framework here this is phrased in terms of ‘conditions’ and ‘actions’. [1] So for example an adsorption requires at least one site to be empty (condition). Then this site can be occupied by CO (action) with a rate constant. Written down in code this looks as follows

kmc_model.add_process(name='CO_adsorption',
               conditions=[Condition(coord=coord, species='empty')],
               actions=[Action(coord=coord, species='CO')],
               rate_constant='p_CO*bar*A/sqrt(2*pi*umass*m_CO/beta)')

Note

In order to ensure correct functioning of the kmcos kMC solver every action should have a corresponding condition for the same coordinate.

Now you might wonder, how come we can simply use m_CO and beta and such. Well, that is because the evaluator will to some trickery to resolve such terms. So beta will be first be translated into 1/(kboltzmann*T) and as long as you have set a parameter T before, this will go through. Same is true for m_CO, here the atomic masses are looked up and added. Note that we need conversion factors of bar and umass.

Then the desorption process is almost the same, except the reverse:

kmc_model.add_process(name='CO_desorption',
               conditions=[Condition(coord=coord, species='CO')],
               actions=[Action(coord=coord, species='empty')],
               rate_constant='p_CO*bar*A/sqrt(2*pi*umass*m_CO/beta)*exp(beta*deltaG*eV)')

To reduce typing, kmcos also knows a shorthand notation for processes. In order to produce the same process you could also type

kmc_model.parse_process('CO_desorption; CO@hollow->empty@hollow ; p_CO*bar*A/sqrt(2*pi*umass*m_CO/beta)*exp(beta*deltaG*eV)')

and since any non-existing on either the left or the right side of the -> symbol is replaced by a corresponding term with the default_species (in this case empty) you could as well type

kmc_model.parse_process('CO_desorption; CO@hollow->; p_CO*bar*A/sqrt(2*pi*umass*m_CO/beta)*exp(beta*deltaG*eV)')

and to make it even shorter you can parse and add the process on one line

kmc_model.parse_and_add_process('CO_desorption; CO@hollow->; p_CO*bar*A/sqrt(2*pi*umass*m_CO/beta)*exp(beta*deltaG*eV)')

In order to add processes on more than one site possible spanning across unit cells, there is a shorthand as well. The full-fledged syntax for each coordinate is

"<site-name>.<offset>.<lattice>"

check Manual generation for details.

Export, save, compile

Before we compile the model, we should specify and understand the various backends that are involved.

local_smart backend (default) for models with <100 processes. lat_int backend for models with >100 processes. (build the model same ways local_smart but different backend for compile step) otf backend requires custom model (build requires different process definitions compared to local_smart) and can work for models which require >10000 processes, since each process rate is calculated on the fly instead of being held in memory.

Here is how we specify the model’s backend

kmc_model.backend = 'local_smart'
kmc_model.backend = 'lat_int'
kmc_model.backend = 'otf'

Next, it’s a good idea to save and compile your work

kmc_model.save_model()
kmcos.compile(kmc_model)

This creates an XML file with the full definition of your model and exports the model to compiled code.

Now is the time to leave the python shell. In the current directory you should see a myfirst_kmc.xml. You will also see a directory ending with _local_smart, this directory includes your compiled model.

You can also skip the model exporting (and do it later) by commenting out kmcos.compile(kmc_model): then you can use a separate python file later. For some installations, you can use kmcos export myfirst_kmc.xml from the linux terminal when you are in the same directory as the XML.

During troubleshooting, exporting separately can sometimes be useful to make sure the compiling occurs gracefully without any line containining an error.

Running and viewing the model

If you now cd to that folder myfirst_kmc_local_smart and run

python3 kmc_settings.py benchmark

You should see that the model was able to run! Next, let’s try seeing how it looks visually with

python3 kmc_settings.py view

The “view” command only works on certain operating systems. For some installations, one can alternativeley type kmcos benchmark and kmcos view.

For running the model, it is recommended to use a runfile.

If you wonder why the CO molecules are basically just dangling there in mid-air that is because you have no background setup, yet. Choose a transition metal of your choice and add it to the lattice setup for extra credit :-).

Wondering where to go from here? If the work-flow makes complete sense, you have a specific model in mind, and just need some more idioms to implement it I suggest you take a look at the examples folder. for some hints. To learn more about the kmcos approach and methods you should into topic guides.

In technical terms, kmcos is run an API via the kmcos python module.

Additionally, though now discouraged, kmcos can be invoked directly from the command line in one of the following ways:

kmcos [help] (all|benchmark|build|edit|export|help|import|rebuild|run|settings-export|shell|version|view|xml) [options]

Taking it home

Despite its simplicity you have now seen all elements needed to implement a kMC model and hopefully gotten a first feeling for the workflow.

[1]	You will have to describe all processes in terms of conditions and actions and you find a more complete description in the topic guide to the process description syntax.

[2]	The description of coordinates follows the simple syntax of the coordinate syntax and the topic guide explains how that works.

An alternative way using .ini files

Presently, a full description of the .ini capability is not being provided because this way is not the standard way of using kmcos. However, it is available. This method is an alternative to making an xml file, and can be used instead of kmcos export.

Prepare a minimal input file with the following content and save it as mini_101.ini

[Meta]
author = Your Name
email = you@server.com
model_dimension = 2
model_name = fcc_100

[Species empty]
color = #FFFFFF

[Species CO]
representation = Atoms("CO", [[0, 0, 0], [0, 0, 1.17]])
color = #FF0000

[Lattice]
cell_size = 3.5 3.5 10.0

[Layer simple_cubic]
site hollow = (0.5, 0.5, 0.5)
color = #FFFFFF

[Parameter k_CO_ads]
value = 100
adjustable = True
min = 1
max = 1e13
scale = log

[Parameter k_CO_des]
value = 100
adjustable = True
min = 1
max = 1e13
scale = log

[Process CO_ads]
rate_constant = k_CO_ads
conditions = empty@hollow
actions = CO@hollow
tof_count = {'adsorption':1}

[Process CO_des]
rate_constant = k_CO_des
conditions = CO@hollow
actions = empty@hollow
tof_count = {'desorption':1}

In the same directory run kmcos export mini_101.ini. You should now have a folder mini_101_local_smart in the same directory. cd into it and run kmcos benchmark. If everything went well you should see something like

Using the [local_smart] backend.
1000000 steps took 1.51 seconds
Or 6.62e+05 steps/s

In the same directory try running kmcos view to watch the model run or fire up kmcos shell to interact with the model interactively. Explore more commands with kmcos help and please refer to the documentation how to build complex model and evaluate them systematically. To test all bells and whistles try kmcos edit mini_101.ini and inspect the model visually.