Expert Adsorption Energies - FAIR Chemistry Documentation

Tutorial Overview

Property	Value
Difficulty	Advanced
Time	45-60 minutes
Prerequisites	Basic catalysis knowledge, Python, ASE
Goal	Reproduce NRR/HER selectivity literature results

One of the most common tasks in computational catalysis is calculating the binding energies or adsorption energies of small molecules on catalyst surfaces.

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

import os
import pickle
import time
from glob import glob

import ase.io
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from ase.optimize import QuasiNewton
from fairchem.core import FAIRChemCalculator, pretrained_mlip
from fairchem.data.oc.core import Adsorbate, AdsorbateSlabConfig, Bulk, Slab
from fairchem.data.oc.utils import DetectTrajAnomaly
from scipy.stats import linregress

# Set random seed to ensure adsorbate enumeration yields a valid candidate
# If using a larger number of random samples this wouldn't be necessary
np.random.seed(22)

Warp 1.18.0 initialized:
   CUDA Toolkit 13.4, Driver 13.1
   Devices:
     "cpu"      : "x86_64"
     "cuda:0"   : "Tesla T4" (16 GiB, sm_75, mempool enabled)
   Kernel cache:
     /home/runner/.cache/warp/1.18.0

Introduction¶

We will reproduce Fig 6b from the following paper: Zhou, Jing, et al. “Enhanced Catalytic Activity of Bimetallic Ordered Catalysts for Nitrogen Reduction Reaction by Perturbation of Scaling Relations.” ACS Catalysis 134 (2023): 2190-2201 (Zhou & others (2023)).

The gist of this figure is a correlation between H* and NNH* adsorbates across many different alloy surfaces. Then, they identify a dividing line between these that separates surfaces known for HER and those known for NRR.

To do this, we will enumerate adsorbate-slab configurations and run ML relaxations on them to find the lowest energy configuration. We will assess parity between the model predicted values and those reported in the paper. Finally we will make the figure and assess separability of the NRR favored and HER favored domains.

Enumerate the adsorbate-slab configurations to run relaxations on¶

Be sure to set the path in fairchem/data/oc/configs/paths.py to point to the correct place or pass the paths as an argument. The database pickles can be found in fairchem/data/oc/databases/pkls (some pkl files are only downloaded by running the command python src/fairchem/core/scripts/download_large_files.py oc from the root of the fairchem repo). We will show one explicitly here as an example and then run all of them in an automated fashion for brevity.

from pathlib import Path

import fairchem.data.oc

db = Path(fairchem.data.oc.__file__).parent / Path("databases/pkls/adsorbates.pkl")
db

PosixPath('/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/databases/pkls/adsorbates.pkl')

Work out a single example¶

We load one bulk id, create a bulk reference structure from it, then generate the surfaces we want to compute.

bulk_src_id = "oqmd-343039"
adsorbate_smiles_nnh = "*N*NH"
adsorbate_smiles_h = "*H"

bulk = Bulk(bulk_src_id_from_db=bulk_src_id, bulk_db_path="NRR_example_bulks.pkl")
adsorbate_H = Adsorbate(
    adsorbate_smiles_from_db=adsorbate_smiles_h, adsorbate_db_path=db
)
adsorbate_NNH = Adsorbate(
    adsorbate_smiles_from_db=adsorbate_smiles_nnh, adsorbate_db_path=db
)
slab = Slab.from_bulk_get_specific_millers(bulk=bulk, specific_millers=(1, 1, 1))
slab

/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)
/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/adsorbate.py:79: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  adsorbate_db = safe_pickle_load(fp)

[Slab: (Ag36Pd12, (1, 1, 1), 0.16666666666666666, True)]

We now need to generate potential placements. We use two kinds of guesses, a heuristic and a random approach. This cell generates 13 potential adsorption geometries.

# Perform heuristic placements
heuristic_adslabs = AdsorbateSlabConfig(slab[0], adsorbate_H, mode="heuristic")

# Perform random placements
# (for AdsorbML we use `num_sites = 100` but we will use 4 for brevity here)
random_adslabs = AdsorbateSlabConfig(
    slab[0], adsorbate_H, mode="random_site_heuristic_placement", num_sites=4
)

adslabs = [*heuristic_adslabs.atoms_list, *random_adslabs.atoms_list]
len(adslabs)

13

Let’s see what we are looking at. It is a little tricky to see the tiny H atom in these figures, but with some inspection you can see there are ontop, bridge, and hollow sites in different places. This is not an exhaustive search; you can increase the number of random placements to check more possibilities. The main idea here is to increase the probability you find the most relevant sites.

from ase.visualize.plot import plot_atoms

fig, axs = plt.subplots(4, 4)

for i, slab in enumerate(adslabs):
    plot_atoms(slab, axs[i % 4, i // 4])
    axs[i % 4, i // 4].set_axis_off()

for i in range(16):
    axs[i % 4, i // 4].set_axis_off()

plt.tight_layout()

Run an ML relaxation¶

We will use an ASE compatible calculator to run these.

Running the model with QuasiNewton prints at each relaxation step which is a lot to print. So we will just run one to demonstrate what happens on each iteration.

os.makedirs(f"data/{bulk_src_id}_{adsorbate_smiles_h}", exist_ok=True)

# Define the
predictor = pretrained_mlip.get_predict_unit("uma-s-1p2p1")
calc = FAIRChemCalculator(predictor, task_name="oc20")

WARNING:root:device was not explicitly set, using device='cuda'.

Now we setup and run the relaxation.

t0 = time.time()
os.makedirs(f"data/{bulk_src_id}_H", exist_ok=True)
adslab = adslabs[0]
adslab.calc = calc
adslab.pbc = True
opt = QuasiNewton(adslab, trajectory=f"data/{bulk_src_id}_H/test.traj")
opt.run(fmax=0.05, steps=100)

print(f"Elapsed time {time.time() - t0:1.1f} seconds")

WARNING:root:Model is being compiled this might take a while for the first time

W1007 02:53:07.533000 9044 site-packages/torch/_logging/_internal.py:1345] [0/0] Profiler record function <class 'torch.autograd.profiler.record_function'> will be ignored

                Step[ FC]     Time          Energy          fmax
BFGSLineSearch:    0[  0] 02:53:59     -130.591267       0.7296

BFGSLineSearch:    1[  2] 02:54:00     -130.736347       0.3707
BFGSLineSearch:    2[  3] 02:54:00     -130.754887       0.1403

BFGSLineSearch:    3[  4] 02:54:00     -130.763494       0.1093
BFGSLineSearch:    4[  6] 02:54:00     -130.767757       0.0776

BFGSLineSearch:    5[  8] 02:54:00     -130.770202       0.0564
BFGSLineSearch:    6[ 10] 02:54:00     -130.771033       0.0485
Elapsed time 55.3 seconds

With a GPU this runs pretty quickly. It is much slower on a CPU.

Run all the systems¶

In principle you can run all the systems now. It takes about an hour though, and we leave that for a later exercise if you want. For now we will run the first two, and for later analysis we provide a results file of all the runs. Let’s read in our reference file and take a look at what is in it.

with open("NRR_example_bulks.pkl", "rb") as f:
    bulks = pickle.load(f)

bulks

[{'atoms': Atoms(symbols='CuPd3', pbc=True, cell=[3.91276645, 3.91276645, 3.91276645], calculator=SinglePointDFTCalculator(...)),
  'src_id': 'oqmd-349719'},
 {'atoms': Atoms(symbols='Pd3Ag', pbc=True, cell=[4.02885979, 4.02885979, 4.02885979], calculator=SinglePointDFTCalculator(...)),
  'src_id': 'oqmd-345911'},
 {'atoms': Atoms(symbols='ScPd3', pbc=True, cell=[4.04684963, 4.04684963, 4.04684963], initial_charges=..., initial_magmoms=..., momenta=..., tags=..., calculator=SinglePointCalculator(...)),
  'src_id': 'mp-2677'},
 {'atoms': Atoms(symbols='Mo3Pd', pbc=True, cell=[3.96898192, 3.96898192, 3.96898192], initial_charges=..., initial_magmoms=..., momenta=..., tags=..., calculator=SinglePointCalculator(...)),
  'src_id': 'mp-1186014'},
 {'atoms': Atoms(symbols='Ag3Pd', pbc=True, cell=[4.14093081, 4.14093081, 4.14093081], calculator=SinglePointCalculator(...)),
  'src_id': 'oqmd-343039'},
 {'src_id': 'oqmd-348629',
  'atoms': Atoms(symbols='Ag3Cu', pbc=True, cell=[4.09439099, 4.09439099, 4.09439099], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-343006',
  'atoms': Atoms(symbols='Ag3Mo', pbc=True, cell=[4.1665424, 4.1665424, 4.1665424], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-349813',
  'atoms': Atoms(symbols='AgCu3', pbc=True, cell=[3.82618693, 3.82618693, 3.82618693], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-347528',
  'atoms': Atoms(symbols='Cu3Ru', pbc=True, cell=[3.72399424, 3.72399424, 3.72399424], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-344251',
  'atoms': Atoms(symbols='PdTa3', pbc=True, cell=[4.13568646, 4.13568646, 4.13568646], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-343394',
  'atoms': Atoms(symbols='AgMo3', pbc=True, cell=[4.00594441, 4.00594441, 4.00594441], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-344635',
  'atoms': Atoms(symbols='Mo3Ru', pbc=True, cell=[3.95617571, 3.95617571, 3.95617571], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-344237',
  'atoms': Atoms(symbols='MoPd3', pbc=True, cell=[3.96059535, 3.96059535, 3.96059535], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-346818',
  'atoms': Atoms(symbols='Pd3Ru', pbc=True, cell=[3.93112559, 3.93112559, 3.93112559], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-349496',
  'atoms': Atoms(symbols='Pd3Ta', pbc=True, cell=[3.9907085, 3.9907085, 3.9907085], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-343615',
  'atoms': Atoms(symbols='MoRu3', pbc=True, cell=[3.85915122, 3.85915122, 3.85915122], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-348366',
  'atoms': Atoms(symbols='AgTa3', pbc=True, cell=[4.1730103, 4.1730103, 4.1730103], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-345352',
  'atoms': Atoms(symbols='AuHf3', pbc=True, cell=[4.36653536, 4.36653536, 4.36653536], calculator=SinglePointDFTCalculator(...))},
 {'src_id': 'oqmd-346653',
  'atoms': Atoms(symbols='AgHf3', pbc=True, cell=[4.39618436, 4.39618436, 4.39618436], calculator=SinglePointDFTCalculator(...))}]

We have 19 bulk materials we will consider. Next we extract the src-id for each one.

bulk_ids = [row["src_id"] for row in bulks]

In theory you would run all of these, but it takes about an hour with a GPU. We provide the relaxation logs and trajectories in the repo for the next step.

These steps are embarrassingly parallel, and can be launched that way to speed things up. The only thing you need to watch is that you don’t exceed the available RAM, which will cause the Jupyter kernel to crash.

The goal here is to relax each candidate adsorption geometry and save the results in a trajectory file we will analyze later. Each trajectory file will have the geometry and final energy of the relaxed structure.

It is somewhat time consuming to run this. We’re going to use a small number of bulks for the testing of this documentation, but otherwise run all of the results for the actual documentation.

import os

fast_docs = os.environ.get("FAST_DOCS", "false").lower() == "true"
if fast_docs:
    num_bulks = 1
    num_sites = 5
    relaxation_steps = 20
else:
    num_bulks = -1
    num_sites = 20
    relaxation_steps = 300

import random
import time

from tqdm import tqdm

tinit = time.time()

random.seed(42)
random.shuffle(bulk_ids)

# Note we're just doing the first bulk_id!
for bulk_src_id in tqdm(bulk_ids[:num_bulks]):

    # Set up data directories
    os.makedirs("data/slabs/", exist_ok=True)
    os.makedirs(f"data/adslabs/{bulk_src_id}_H", exist_ok=True)
    os.makedirs(f"data/adslabs/{bulk_src_id}_NNH", exist_ok=True)

    # Enumerate slabs and establish adsorbates
    bulk = Bulk(bulk_src_id_from_db=bulk_src_id, bulk_db_path="NRR_example_bulks.pkl")
    slab = Slab.from_bulk_get_specific_millers(bulk=bulk, specific_millers=(1, 1, 1))

    slab_atoms = slab[0].atoms.copy()
    slab_atoms.calc = calc
    slab_atoms.pbc = True
    opt = QuasiNewton(
        slab_atoms,
        trajectory=f"data/slabs/{bulk_src_id}.traj",
        logfile=f"data/slabs/{bulk_src_id}.log",
    )
    opt.run(fmax=0.05, steps=relaxation_steps)
    print(
        f"  Elapsed time: {time.time() - t0:1.1f} seconds for data/slabs/{bulk_src_id} slab relaxation"
    )

    # Perform heuristic placements
    heuristic_adslabs_H = AdsorbateSlabConfig(
        slab[0],
        adsorbate_H,
        mode="random_site_heuristic_placement",
        num_sites=num_sites,
    )
    heuristic_adslabs_NNH = AdsorbateSlabConfig(
        slab[0],
        adsorbate_NNH,
        mode="random_site_heuristic_placement",
        num_sites=num_sites,
    )

    print(f"{len(heuristic_adslabs_H.atoms_list)} H slabs to compute for {bulk_src_id}")
    print(
        f"{len(heuristic_adslabs_NNH.atoms_list)} NNH slabs to compute for {bulk_src_id}"
    )

    for idx, adslab in enumerate(heuristic_adslabs_H.atoms_list):
        t0 = time.time()
        adslab.calc = calc
        adslab.pbc = True
        print(f"Running data/adslabs/{bulk_src_id}_H/{idx}")
        opt = QuasiNewton(
            adslab,
            trajectory=f"data/adslabs/{bulk_src_id}_H/{idx}.traj",
            logfile=f"data/adslabs/{bulk_src_id}_H/{idx}.log",
        )
        opt.run(fmax=0.05, steps=200)
        print(
            f"  Elapsed time: {time.time() - t0:1.1f} seconds for data/adslabs/{bulk_src_id}_H/{idx}"
        )

    for idx, adslab in enumerate(heuristic_adslabs_NNH.atoms_list):
        t0 = time.time()
        adslab.calc = calc
        adslab.pbc = True
        print(f"Running data/adslabs/{bulk_src_id}_NNH/{idx}")
        opt = QuasiNewton(
            adslab,
            trajectory=f"data/adslabs/{bulk_src_id}_NNH/{idx}.traj",
            logfile=f"data/adslabs/{bulk_src_id}_NNH/{idx}.log",
        )
        opt.run(fmax=0.05, steps=relaxation_steps)
        print(
            f"  Elapsed time: {time.time() - t0:1.1f} seconds for data/adslabs/{bulk_src_id}_NNH/{idx}"
        )

print(f"Elapsed time: {time.time() - tinit:1.1f} seconds")

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

WARNING:root:The UMA fast path (merge_mole + compile) is only available for fixed composition, task, charge, and spin. This is optimized for MD applications. Falling back to a less optimized version for subsequent evaluations. Reason: 'Compositions differ from merged model'.
Use inference_settings='batch' for heterogeneous batched evaluations.

  Elapsed time: 64.6 seconds for data/slabs/oqmd-349496 slab relaxation

20 H slabs to compute for oqmd-349496
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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

  Elapsed time: 10.1 seconds for data/slabs/oqmd-346818 slab relaxation

20 H slabs to compute for oqmd-346818
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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
  bulk_db = safe_pickle_load(fp)

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/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/fairchem/data/oc/core/bulk.py:66: UserWarning: Loading data from a pickle file. Pickle files can execute arbitrary code and should only be loaded from trusted sources. Consider migrating to a safer format such as Parquet, CSV, or JSON.
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Parse the trajectories and post-process¶

As a post-processing step we check to see if:

the adsorbate desorbed
the adsorbate disassociated
the adsorbate intercalated
the surface has changed

We check these because they affect our referencing scheme and may result in energies that don’t mean what we think, e.g. they aren’t just adsorption, but include contributions from other things like desorption, dissociation or reconstruction. For (4), the relaxed surface should really be supplied as well. It will be necessary when correcting the SP / RX energies later. Since we don’t have it here, we will ommit supplying it, and the detector will instead compare the initial and final slab from the adsorbate-slab relaxation trajectory. If a relaxed slab is provided, the detector will compare it and the slab after the adsorbate-slab relaxation. The latter is more correct!

To compute the adsorption energies using the total energy UMA-OC20 model, we’ll need the gas-phase reference energies from OC20 (see the original paper!). You could also calculate these quickly in DFT using a linear combination of H2O, H2, N2, and CO.

# reference energies from a linear combination of H2O/N2/CO/H2!
atomic_reference_energies = {
    "H": -3.477,
    "N": -8.083,
    "O": -7.204,
    "C": -7.282,
}

In this loop we find the most stable (most negative) adsorption energy for each adsorbate on each surface and save them in a DataFrame.

# Iterate over trajs to extract results
min_E = []
for file_outer in glob("data/adslabs/*"):
    ads = file_outer.split("_")[1]
    bulk = file_outer.split("/")[-1].split("_")[0]

    slab = ase.io.read(f"data/slabs/{bulk}.traj")
    results = []
    for file in glob(f"{file_outer}/*.traj"):
        rx_id = file.split("/")[-1].split(".")[0]
        traj = ase.io.read(file, ":")

        # Check to see if the trajectory is anomolous
        detector = DetectTrajAnomaly(traj[0], traj[-1], traj[0].get_tags())
        anom = (
            detector.is_adsorbate_dissociated()
            or detector.is_adsorbate_desorbed()
            or detector.has_surface_changed()
            or detector.is_adsorbate_intercalated()
        )
        rx_energy = (
            traj[-1].get_potential_energy()
            - slab.get_potential_energy()
            - sum(
                [
                    atomic_reference_energies[x]
                    for x in traj[0][traj[0].get_tags() == 2].get_chemical_symbols()
                ]
            )
        )

        results.append(
            {
                "relaxation_idx": rx_id,
                "relaxed_atoms": traj[-1],
                "relaxed_energy_ml": rx_energy,
                "anomolous": anom,
            }
        )
    df = pd.DataFrame(results)

    df = df[~df.anomolous].copy().reset_index()
    min_e = min(df.relaxed_energy_ml.tolist())
    min_E.append({"adsorbate": ads, "bulk_id": bulk, "min_E_ml": min_e})

df = pd.DataFrame(min_E)
df_h = df[df.adsorbate == "H"]
df_nnh = df[df.adsorbate == "NNH"]
df_flat = df_h.merge(df_nnh, on="bulk_id")

Make parity plots for values obtained by ML v. reported in the paper¶

# Add literature data to the dataframe
with open("literature_data.pkl", "rb") as f:
    literature_data = pickle.load(f)
df_all = df_flat.merge(pd.DataFrame(literature_data), on="bulk_id")

f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
f.set_figheight(15)
x = df_all.min_E_ml_x.tolist()
y = df_all.E_lit_H.tolist()
ax1.set_title("*H parity")
ax1.plot([-3.5, 2], [-3.5, 2], "k-", linewidth=3)
slope, intercept, r, p, se = linregress(x, y)
ax1.plot(
    [-3.5, 2],
    [
        -3.5 * slope + intercept,
        2 * slope + intercept,
    ],
    "k--",
    linewidth=2,
)

ax1.legend(
    [
        "y = x",
        f"y = {slope:1.2f} x + {intercept:1.2f}, R-sq = {r**2:1.2f}",
    ],
    loc="upper left",
)
ax1.scatter(x, y)
ax1.axis("square")
ax1.set_xlim([-3.5, 2])
ax1.set_ylim([-3.5, 2])
ax1.set_xlabel("dE predicted UMA [eV]")
ax1.set_ylabel("dE NRR paper [eV]")


x = df_all.min_E_ml_y.tolist()
y = df_all.E_lit_NNH.tolist()
ax2.set_title("*N*NH parity")
ax2.plot([-3.5, 2], [-3.5, 2], "k-", linewidth=3)
slope, intercept, r, p, se = linregress(x, y)
ax2.plot(
    [-3.5, 2],
    [
        -3.5 * slope + intercept,
        2 * slope + intercept,
    ],
    "k--",
    linewidth=2,
)

ax2.legend(
    [
        "y = x",
        f"y = {slope:1.2f} x + {intercept:1.2f}, R-sq = {r**2:1.2f}",
    ],
    loc="upper left",
)
ax2.scatter(x, y)
ax2.axis("square")
ax2.set_xlim([-3.5, 2])
ax2.set_ylim([-3.5, 2])
ax2.set_xlabel("dE predicted UMA [eV]")
ax2.set_ylabel("dE NRR paper [eV]")
f.set_figwidth(15)
f.set_figheight(7)

Make figure 6b and compare to literature results¶

f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
x = df_all[df_all.reaction == "HER"].min_E_ml_y.tolist()
y = df_all[df_all.reaction == "HER"].min_E_ml_x.tolist()
comp = df_all[df_all.reaction == "HER"].composition.tolist()

ax1.scatter(x, y, c="r", label="HER")
for i, txt in enumerate(comp):
    ax1.annotate(txt, (x[i], y[i]))

x = df_all[df_all.reaction == "NRR"].min_E_ml_y.tolist()
y = df_all[df_all.reaction == "NRR"].min_E_ml_x.tolist()
comp = df_all[df_all.reaction == "NRR"].composition.tolist()
ax1.scatter(x, y, c="b", label="NRR")
for i, txt in enumerate(comp):
    ax1.annotate(txt, (x[i], y[i]))


ax1.legend()
ax1.set_xlabel("dE *N*NH predicted UMA [eV]")
ax1.set_ylabel("dE *H predicted UMA [eV]")


x = df_all[df_all.reaction == "HER"].E_lit_NNH.tolist()
y = df_all[df_all.reaction == "HER"].E_lit_H.tolist()
comp = df_all[df_all.reaction == "HER"].composition.tolist()

ax2.scatter(x, y, c="r", label="HER")
for i, txt in enumerate(comp):
    ax2.annotate(txt, (x[i], y[i]))

x = df_all[df_all.reaction == "NRR"].E_lit_NNH.tolist()
y = df_all[df_all.reaction == "NRR"].E_lit_H.tolist()
comp = df_all[df_all.reaction == "NRR"].composition.tolist()
ax2.scatter(x, y, c="b", label="NRR")
for i, txt in enumerate(comp):
    ax2.annotate(txt, (x[i], y[i]))

ax2.legend()
ax2.set_xlabel("dE *N*NH literature [eV]")
ax2.set_ylabel("dE *H literature [eV]")
f.set_figwidth(15)
f.set_figheight(7)

References¶

Zhou, J., & others. (2023). Enhanced Catalytic Activity of Bimetallic Ordered Catalysts for Nitrogen Reduction Reaction by Perturbation of Scaling Relations. 10.1021/acscatal.2c05877