Intro to Flows¶
In addition to individual jobs, quacc ships with a variety of pre-made workflows, which we call flows for short. Here, we will run a pre-made flow with EMT, defined by quacc.recipes.emt.slabs.bulk_to_slabs_flow and described by the following graph representation:
graph LR
A[Input] --> B(Make Slabs)
B --> C(Slab Relax) --> G(Slab Static) --> K[Output]
B --> D(Slab Relax) --> H(Slab Static) --> K[Output]
B --> E(Slab Relax) --> I(Slab Static) --> K[Output]
B --> F(Slab Relax) --> J(Slab Static) --> K[Output];Since we are running everything locally for now, every calculation will be run serially. As discussed in the following sections, using a workflow manager will allow us to run these calculations in parallel on one or more remote machines.
A Representative Example¶
Like before, we import the pre-made recipe, pass it the Atoms object, and then print the results.
from ase.build import bulk
from quacc.recipes.emt.slabs import bulk_to_slabs_flow
# Define the Atoms object
atoms = bulk("Cu")
# Define the workflow
result = bulk_to_slabs_flow(atoms)
# Print the result
print(result)
Modifying the Parameters for Jobs in a Pre-Made Flow
To modify the default parameters of a subset of jobs in a pre-made workflow, you can pass a dictionary of parameters to the job_params keyword argument of the workflow function. For example, to tighten the force tolerance of the relax_job step in the aforementioned recipe, you can do the following:
To modify the default parameters of all the jobs in a pre-made workflow, such as the EMT calculator's asap_cutoff paramter, you can use the "all" keyword as a shorthand:
Concluding Comments¶
At this point, you now have the basic idea of how quacc recipes work!
If you don't care about using a workflow engine, feel free to write simple quacc-based Python scripts and submit them as-is using your favorite computing machine and scheduler. However, if you are looking to efficiently scale up and monitor large numbers of workflows, continue reading!