Elastic Tensors - FAIR Chemistry Documentation

Let’s do something more interesting that normally takes quite a bit of work in DFT: calculating an elastic constant! Elastic properties are important to understand how strong or easy to deform a material is, or how a material might change if compressed or expanded in specific directions (i.e. the Poisson ratio!).

We don’t have to change much code from above, we just use a built-in recipe to calculate the elastic tensor from quacc. This recipe

(optionally) Relaxes the unit cell using the MLIP
Generates a number of deformed unit cells by applying strains
For each deformation, a relaxation using the MLIP and (optionally) a single point calculation is run
Finally, all of the above calculations are used to calculate the elastic properties of the material

For more documentation, see the quacc docs for quacc.recipes.mlp.elastic_tensor_flow

Need to install fairchem-core or get UMA access or getting permissions/401 errors?

Install the necessary packages using pip, uv etc

! pip install fairchem-core fairchem-data-oc fairchem-applications-cattsunami

Get access to any necessary huggingface gated models
- Get and login to your Huggingface account
- Request access to https://huggingface.co/facebook/UMA
- Create a Huggingface token at https://huggingface.co/settings/tokens/ with the permission “Permissions: Read access to contents of all public gated repos you can access”
- Add the token as an environment variable using huggingface-cli login or by setting the HF_TOKEN environment variable.

# Login using the huggingface-cli utility
! huggingface-cli login

# alternatively,
import os
os.environ['HF_TOKEN'] = 'MY_TOKEN'

from __future__ import annotations

from ase.build import bulk
from quacc.recipes.mlp.elastic import elastic_tensor_flow

# Make an Atoms object of a bulk Cu structure
atoms = bulk("Cu")

# Run an elastic property calculation with our favorite MLP potential
result = elastic_tensor_flow(
    atoms,
    job_params={
        "all": dict(
            method="fairchem",
            name_or_path="uma-s-1p2",
            task_name="omat",
        ),
    },
)

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 4
      1 from __future__ import annotations
      2 
      3 from ase.build import bulk
----> 4 from quacc.recipes.mlp.elastic import elastic_tensor_flow
      5 
      6 # Make an Atoms object of a bulk Cu structure
      7 atoms = bulk("Cu")

ModuleNotFoundError: No module named 'quacc.recipes.mlp'

result["elasticity_doc"].bulk_modulus

Congratulations, you ran your first elastic tensor calculation!