Using FAIR chemistry models and workflow tools like quacc for inorganic materials science#
We’re going to use quacc along with FAIR chem calculators for some simple recipes to calculate elastic and phonon properties. quacc has the nice property that you can also use many different workflow managers like jobflow, dask, or prefect to scale and parallelize many calculations, including both ML and DFT calculations.
Quick setup (without a workflow backend)#
First, make sure you installed
fairchem[quacc]to pick up an appropriate version of quacc and the phonon dependencies.We’re going to use OMat24 machine learning interatomic potentials (MLIPs), which require approval and a license agreement.
Navigate to the OMat24 model repo, login with your HuggingFace account, and request access to the gated models if you haven’t already
Make sure
huggingface_hubhas access to the repo by runninghuggingface-cli loginand following the instructions: a. Navigate to HuggingFace tokens b. Click “+Create new token” c. type OMat24 in the Repositories permissions field d. Click create token e. Type the token into the CLI, or alternatively you can runhuggingface-cli login --token YOUR_TOKEN_HERE. You can also set the environment variableHF_TOKEN=YOUR_TOKEN_HEREif that’s more convenient.
Set up quacc to use your favorite workflow tool (dask, prefect, etc) by setting your quacc configuration. a. Tip: If you just want to run like this tutorial is, write a simple file with
WORKFLOW_ENGINE: nullin~/.quacc.yaml. This is by far the easiest way to get started if you don’t want to worry about parallelization/etc
Example of a simple relaxation#
In quacc, a job is a fundamental block of work that can be run in parallel with other jobs. A flow is a sequence of jobs that need to be run to accomplish a goal. See the quacc docs for more information about how this works!
We’re going to start simple here - let’s run a local relaxation (optimize the unit cell and positions) using a pre-trained EquiformerV2-31M-OMAT24-MP-sAlex checkpoint. This checkpoint has a few fun properties
It’s a relatively small (31M) parameter model
It was pre-trained on the OMat24 dataset, and then fine-tuned on the MPtrj and Alexandria datasets, so it should emit energies and forces that are consistent with the MP GGA (PBE/PBE+U) level of theory
This code will download the appropriate checkpoint from huggingface_hub automatically; if you don’t have the right access token specified, you’ll hit an permission or 401 error.
import pprint
from ase.build import bulk
from ase.optimize import LBFGS
from quacc.recipes.mlp.core import relax_job
# Make an Atoms object of a bulk Cu structure
atoms = bulk("Cu")
# Run a structure relaxation
result = relax_job(
atoms,
method="fairchem",
model_name="EquiformerV2-31M-OMAT24-MP-sAlex",
local_cache="./fairchem_checkpoint_cache/",
opt_params={"fmax": 1e-3, "optimizer": LBFGS},
)
WARNING:quacc.recipes.mlp._base:CUDA is not available to PyTorch. Calculations will be slow.
INFO:root:Project root: /home/runner/work/fairchem/fairchem/src/fairchem
/home/runner/work/fairchem/fairchem/src/fairchem/core/models/scn/spherical_harmonics.py:23: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
_Jd = torch.load(os.path.join(os.path.dirname(__file__), "Jd.pt"))
/home/runner/work/fairchem/fairchem/src/fairchem/core/models/equiformer_v2/wigner.py:10: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
_Jd = torch.load(os.path.join(os.path.dirname(__file__), "Jd.pt"))
/home/runner/work/fairchem/fairchem/src/fairchem/core/models/escn/so3.py:23: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
_Jd = torch.load(os.path.join(os.path.dirname(__file__), "Jd.pt"))
INFO:root:Checking local cache: ./fairchem_checkpoint_cache/ for model EquiformerV2-31M-OMAT24-MP-sAlex
/home/runner/work/fairchem/fairchem/src/fairchem/core/common/relaxation/ase_utils.py:200: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
checkpoint = torch.load(checkpoint_path, map_location=torch.device("cpu"))
INFO:root:amp: true
cmd:
checkpoint_dir: /home/runner/work/fairchem/fairchem/docs/core/checkpoints/2025-03-29-04-41-36
commit: core:b88b66e,experimental:NA
identifier: ''
logs_dir: /home/runner/work/fairchem/fairchem/docs/core/logs/wandb/2025-03-29-04-41-36
print_every: 100
results_dir: /home/runner/work/fairchem/fairchem/docs/core/results/2025-03-29-04-41-36
seed: null
timestamp_id: 2025-03-29-04-41-36
version: 0.1.dev1+gb88b66e
dataset:
a2g_args:
r_energy: true
r_forces: true
r_stress: true
format: ase_db
transforms:
decompose_tensor:
decomposition:
stress_anisotropic:
irrep_dim: 2
stress_isotropic:
irrep_dim: 0
rank: 2
tensor: stress
element_references:
file: /fsx-ocp-med/shared/alex-10M/alex-mp-norms-refs/element_references.pt
normalizer:
file: /fsx-ocp-med/shared/alex-10M/alex-mp-norms-refs/normalizers.pt
evaluation_metrics:
metrics:
energy:
- mae
- mae_density
forces:
- mae
- forcesx_mae
- forcesy_mae
- forcesz_mae
- cosine_similarity
stress:
- mae
- mae_density
stress_anisotropic:
- mae
stress_isotropic:
- mae
primary_metric: energy_mae
gp_gpus: null
gpus: 0
logger: wandb
loss_functions:
- energy:
coefficient: 20
fn: mae_density
- forces:
coefficient: 10
fn: l2mae
- stress_isotropic:
coefficient: 1
fn: mae
- stress_anisotropic:
coefficient: 1
fn: mae
reduction: mean_all
model:
backbone:
alpha_drop: 0.1
attn_activation: silu
attn_alpha_channels: 64
attn_hidden_channels: 64
attn_value_channels: 16
avg_degree: 61.94676351484548
avg_num_nodes: 31.16592360068011
distance_function: gaussian
drop_path_rate: 0.1
edge_channels: 128
enforce_max_neighbors_strictly: false
ffn_activation: silu
ffn_hidden_channels: 128
grid_resolution: 18
lmax_list:
- 4
max_neighbors: 20
max_num_elements: 96
max_radius: 12.0
mmax_list:
- 2
model: equiformer_v2_backbone
norm_type: layer_norm_sh
num_distance_basis: 512
num_heads: 8
num_layers: 8
num_sphere_samples: 128
otf_graph: true
proj_drop: 0.0
share_atom_edge_embedding: false
sphere_channels: 128
use_atom_edge_embedding: true
use_attn_renorm: true
use_gate_act: false
use_grid_mlp: true
use_m_share_rad: false
use_pbc: true
use_pbc_single: true
use_s2_act_attn: false
use_sep_s2_act: true
weight_init: uniform
heads:
energy:
module: fairchem.core.models.equiformer_v2.heads.EqV2ScalarHead
forces:
module: fairchem.core.models.equiformer_v2.heads.EqV2VectorHead
stress:
decompose: true
module: fairchem.core.models.equiformer_v2.heads.Rank2SymmetricTensorHead
output_name: stress
use_source_target_embedding: true
name: hydra
otf_graph: true
pass_through_head_outputs: true
optim:
batch_size: 8
clip_grad_norm: 100
ema_decay: 0.999
eval_batch_size: 12
eval_every: 3000
load_balancing: atoms
lr_initial: 0.0002
max_epochs: 16
num_workers: 8
optimizer: AdamW
optimizer_params:
weight_decay: 0.001
scheduler: LambdaLR
scheduler_params:
epochs: 741904
lambda_type: cosine
lr: 0.0002
lr_min_factor: 0.01
warmup_epochs: 463
warmup_factor: 0.2
outputs:
energy:
level: system
property: energy
forces:
eval_on_free_atoms: true
level: atom
property: forces
train_on_free_atoms: true
stress:
decomposition:
stress_anisotropic:
eval_on_free_atoms: true
irrep_dim: 2
level: system
parent: stress
train_on_free_atoms: true
stress_isotropic:
eval_on_free_atoms: true
irrep_dim: 0
level: system
parent: stress
train_on_free_atoms: true
level: system
property: stress
relax_dataset: {}
slurm:
account: ocp
cpus_per_task: 9
folder: /fsx-ocp-med/lbluque/logs/omat-alex-mp/S2EFS/train/4478177
gpus_per_node: 8
job_id: '4478177'
job_name: eqV2_31M_ft_alexmptraj_e20_f10_s1_cos16
mem: 480GB
nodes: 4
ntasks_per_node: 8
partition: learn
qos: ocp_high
time: 4320
task: {}
test_dataset: {}
trainer: ocp
val_dataset: {}
INFO:root:Loading model: hydra
INFO:root:Loaded HydraModel with 31207434 parameters.
INFO:root:Loading checkpoint in inference-only mode, not loading keys associated with trainer state!
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-28-280071-91021
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-28-280071-91021
pprint.pprint(result)
{'atoms': Atoms(symbols='Cu', pbc=True, cell=[[0.0, 1.805, 1.805], [1.805, 0.0, 1.805], [1.805, 1.805, 0.0]]),
'converged': True,
'dir_name': '/home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-28-280071-91021',
'input_atoms': {'atoms': Atoms(symbols='Cu', pbc=True, cell=[[0.0, 1.805, 1.805], [1.805, 0.0, 1.805], [1.805, 1.805, 0.0]]),
'structure_metadata': {'builder_meta': {'build_date': datetime.datetime(2025, 3, 29, 4, 42, 28, 773179, tzinfo=datetime.timezone.utc),
'emmet_version': '0.84.6rc6',
'pymatgen_version': '2025.1.9'},
'chemsys': 'Cu',
'composition': Composition('Cu1'),
'composition_reduced': Composition('Cu1'),
'density': 8.971719813140734,
'density_atomic': 11.761470249999999,
'elements': [Element Cu],
'formula_anonymous': 'A',
'formula_pretty': 'Cu',
'nelements': 1,
'nsites': 1,
'symmetry': {'angle_tolerance': 5.0,
'crystal_system': <CrystalSystem.cubic: 'Cubic'>,
'number': 225,
'point_group': 'm-3m',
'symbol': 'Fm-3m',
'symprec': 0.1,
'version': '2.6.0'},
'volume': 11.761470249999999}},
'name': 'fairchem Relax',
'nid': 'fv-az1377-956.04mcjwnfvh2ullqrygi5qw4vjc.dx.internal.cloudapp.net',
'parameters': {'version': '0.1.dev1+gb88b66e'},
'parameters_opt': {'fmax': 0.001,
'max_steps': 1000,
'maxstep': 0.2,
'optimizer': 'LBFGS',
'type': 'optimization'},
'quacc_version': '0.13.0',
'results': {'energy': -4.098316669464111,
'forces': array([[1.0281279e-08, 5.0602549e-09, 7.6041227e-09]], dtype=float32),
'stress': array([[-1.1523021e-02, -1.5406982e-08, -1.5228842e-08],
[-1.5406982e-08, -1.1515909e-02, -1.8953735e-08],
[-1.5228842e-08, -1.8953735e-08, -1.1517838e-02]], dtype=float32)},
'structure_metadata': {'builder_meta': {'build_date': datetime.datetime(2025, 3, 29, 4, 42, 28, 881344, tzinfo=datetime.timezone.utc),
'emmet_version': '0.84.6rc6',
'pymatgen_version': '2025.1.9'},
'chemsys': 'Cu',
'composition': Composition('Cu1'),
'composition_reduced': Composition('Cu1'),
'density': 8.971719813140734,
'density_atomic': 11.761470249999999,
'elements': [Element Cu],
'formula_anonymous': 'A',
'formula_pretty': 'Cu',
'nelements': 1,
'nsites': 1,
'symmetry': {'angle_tolerance': 5.0,
'crystal_system': <CrystalSystem.cubic: 'Cubic'>,
'number': 225,
'point_group': 'm-3m',
'symbol': 'Fm-3m',
'symprec': 0.1,
'version': '2.6.0'},
'volume': 11.761470249999999},
'trajectory': [Atoms(symbols='Cu', pbc=True, cell=[[0.0, 1.805, 1.805], [1.805, 0.0, 1.805], [1.805, 1.805, 0.0]], calculator=SinglePointCalculator(...))],
'trajectory_results': [{'energy': -4.098316669464111,
'forces': array([[1.02812789e-08, 5.06025488e-09, 7.60412266e-09]]),
'stress': array([[-1.15230214e-02, -1.54069824e-08, -1.52288422e-08],
[-1.54069824e-08, -1.15159089e-02, -1.89537346e-08],
[-1.52288422e-08, -1.89537346e-08, -1.15178376e-02]])}]}
Congratulations; you ran your first relaxation using an OMat24-trained checkpoint and quacc!
Example of an elastic property calculation#
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
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",
model_name="EquiformerV2-31M-OMAT24-MP-sAlex",
local_cache="./fairchem_checkpoint_cache/",
),
},
)
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-29-014193-94025
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-29-014193-94025
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-29-521066-65281
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-29-521066-65281
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-29-799669-49733
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-29-799669-49733
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-30-071104-86097
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-30-071104-86097
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-30-370065-42816
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-30-370065-42816
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-30-646127-59375
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-30-646127-59375
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-30-926814-95039
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-30-926814-95039
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-31-419424-65094
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-31-419424-65094
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-31-676970-28365
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-31-676970-28365
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-31-931512-55684
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-31-931512-55684
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-32-180600-13874
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-32-180600-13874
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-32-453423-49334
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-32-453423-49334
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-32-708258-88252
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-32-708258-88252
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-32-963290-47596
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-32-963290-47596
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-33-210710-93637
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-33-210710-93637
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-33-507579-23270
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-33-507579-23270
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-33-775016-20225
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-33-775016-20225
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-34-036125-29167
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-34-036125-29167
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-34-299148-24414
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-34-299148-24414
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-34-587581-60725
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-34-587581-60725
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-35-082854-23214
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-35-082854-23214
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-35-321556-28839
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-35-321556-28839
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-35-559138-33225
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-35-559138-33225
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-35-834939-95612
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-35-834939-95612
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-36-069635-76583
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-36-069635-76583
result["elasticity_doc"].bulk_modulus
BulkModulus(voigt=188.065, reuss=188.065, vrh=188.065)
Congratulations, you ran your first elastic tensor calculation!
Example of a phonon calculation#
Phonon calculations are very important for inorganic materials science to
Calculate thermal conductivity
Understand the vibrational modes, and thus entropy and free energy, of a material
Predict the stability of a material at finite temperature (e.g. 300 K) among many others! We can run a similarly straightforward calculation that
Runs a relaxation on the unit cell and atoms
Repeats the unit cell a number of times to make it sufficiently large to capture many interesting vibrational models
Generatives a number of finite displacement structures by moving each atom of the unit cell a little bit in each direction
Running single point calculations on each of (3)
Gathering all of the calculations and calculating second derivatives (the hessian matrix!)
Calculating the eigenvalues/eigenvectors of the hessian matrix to find the vibrational modes of the material
Analyzing the thermodynamic properties of the vibrational modes.
Note that this analysis assumes that all vibrational modes are harmonic, which is a pretty reasonable approximately for low/moderate temperature materials, but becomes less realistic at high temperatures.
from ase.build import bulk
from quacc.recipes.mlp.phonons import phonon_flow
# Make an Atoms object of a bulk Cu structure
atoms = bulk("Cu")
# Run a phonon (hessian) calculation with our favorite MLP potential
result = phonon_flow(
atoms,
method="fairchem",
job_params={
"all": dict(
model_name="EquiformerV2-31M-OMAT24-MP-sAlex",
local_cache="./fairchem_checkpoint_cache/",
),
},
min_lengths=10.0, # set the minimum unit cell size smaller to be compatible with limited github runner ram
)
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-41-742472-18819
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-41-742472-18819
INFO:quacc.runners.prep:Calculation will run at /home/runner/work/fairchem/fairchem/docs/core/tmp-quacc-2025-03-29-04-42-46-233908-65385
/opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/seekpath/hpkot/__init__.py:172: DeprecationWarning: dict interface is deprecated. Use attribute interface instead
conv_lattice = dataset["std_lattice"]
INFO:quacc.runners.prep:Calculation results stored at /home/runner/work/fairchem/fairchem/docs/core/quacc-2025-03-29-04-42-46-233908-65385
print(
f'The entropy at { result["results"]["thermal_properties"]["temperatures"][-1]:.0f} K is { result["results"]["thermal_properties"]["entropy"][-1]:.2f} kJ/mol'
)
The entropy at 1000 K is 64.56 kJ/mol
Congratulations, you ran your first phonon calculation!
Parallelizing these calculations#
These calculations are super straightforward to parallelize because of how quacc is written. Simply choose a workflow manager like parssl or prefect in quacc, and run the same code! There are many ways to run these calculations in parallel. The FAIR chemistry team regularly runs hundreds of thousands of calculations in some of these packages at scale.