Criegee22 Benchmark

ml_peg/analysis/molecular_reactions/Criegee22/analyse_Criegee22.py

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+"""
+Analyse Criegee22 reaction barrier benchmark.
+
+10.1021/acs.jpca.8b09349
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from ase import units
+from ase.io import read, write
+import pytest
+
+from ml_peg.analysis.utils.decorators import build_table, plot_parity
+from ml_peg.analysis.utils.utils import build_d3_name_map, load_metrics_config, mae
+from ml_peg.app import APP_ROOT
+from ml_peg.calcs import CALCS_ROOT
+from ml_peg.models.get_models import load_models
+from ml_peg.models.models import current_models
+
+MODELS = load_models(current_models)
+D3_MODEL_NAMES = build_d3_name_map(MODELS)
+
+EV_TO_KCAL = units.mol / units.kcal
+CALC_PATH = CALCS_ROOT / "molecular_reactions" / "Criegee22" / "outputs"
+OUT_PATH = APP_ROOT / "data" / "molecular_reactions" / "Criegee22"
+
+METRICS_CONFIG_PATH = Path(__file__).with_name("metrics.yml")
+DEFAULT_THRESHOLDS, DEFAULT_TOOLTIPS, DEFAULT_WEIGHTS = load_metrics_config(
+    METRICS_CONFIG_PATH
+)
+
+
+def labels() -> list:
+    """
+    Get list of system names.
+
+    Returns
+    -------
+    list
+        List of all system names.
+    """
+    for model_name in MODELS:
+        labels_list = [
+            path.stem for path in sorted((CALC_PATH / model_name).glob("*.xyz"))
+        ]
+        break
+    return labels_list
+
+
+@pytest.fixture
+@plot_parity(
+    filename=OUT_PATH / "figure_criegee22_barriers.json",
+    title="Reaction barriers",
+    x_label="Predicted barrier / kcal/mol",
+    y_label="Reference barrier / kcal/mol",
+    hoverdata={
+        "Labels": labels(),
+    },
+)
+def barrier_heights() -> dict[str, list]:
+    """
+    Get barrier heights for all systems.
+
+    Returns
+    -------
+    dict[str, list]
+        Dictionary of all reference and predicted barrier heights.
+    """
+    results = {"ref": []} | {mlip: [] for mlip in MODELS}
+    ref_stored = False
+
+    for model_name in MODELS:
+        for label in labels():
+            structs = read(CALC_PATH / model_name / f"{label}.xyz", index=":")
+
+            results[model_name].append(
+                (structs[1].info["model_energy"] - structs[0].info["model_energy"])
+                * EV_TO_KCAL
+            )
+            if not ref_stored:
+                results["ref"].append(
+                    (structs[1].info["ref_energy"] - structs[0].info["ref_energy"])
+                    * EV_TO_KCAL
+                )
+
+            # Write structures for app
+            structs_dir = OUT_PATH / model_name
+            structs_dir.mkdir(parents=True, exist_ok=True)
+            write(structs_dir / f"{label}_rct.xyz", structs)
+        ref_stored = True
+    return results
+
+
+@pytest.fixture
+def get_mae(barrier_heights) -> dict[str, float]:
+    """
+    Get mean absolute error for barrier heights.
+
+    Parameters
+    ----------
+    barrier_heights
+        Dictionary of reference and predicted barrier heights.
+
+    Returns
+    -------
+    dict[str, float]
+        Dictionary of predicted barrier height errors for all models.
+    """
+    results = {}
+    for model_name in MODELS:
+        results[model_name] = mae(barrier_heights["ref"], barrier_heights[model_name])
+    return results
+
+
+@pytest.fixture
+@build_table(
+    filename=OUT_PATH / "criegee22_barriers_metrics_table.json",
+    metric_tooltips=DEFAULT_TOOLTIPS,
+    thresholds=DEFAULT_THRESHOLDS,
+    mlip_name_map=D3_MODEL_NAMES,
+)
+def metrics(get_mae: dict[str, float]) -> dict[str, dict]:
+    """
+    Get all metrics.
+
+    Parameters
+    ----------
+    get_mae
+        Mean absolute errors for all models.
+
+    Returns
+    -------
+    dict[str, dict]
+        Metric names and values for all models.
+    """
+    return {"MAE": get_mae}
+
+
+def test_criegee22_barriers(metrics: dict[str, dict]) -> None:
+    """
+    Run Criegee22 barriers test.
+
+    Parameters
+    ----------
+    metrics
+        All new benchmark metric names and dictionary of values for each model.
+    """
+    return

ml_peg/analysis/molecular_reactions/Criegee22/metrics.yml

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+metrics:
+  MAE:
+    good: 0.0
+    bad: 20.0
+    unit: kcal/mol
+    tooltip: Mean Absolute Error for all systems
+    level_of_theory: CCSDT(Q)/CBS

ml_peg/app/molecular_reactions/Criegee22/app_Criegee22.py

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+"""Run Criegee22 app."""
+
+from __future__ import annotations
+
+from dash import Dash
+from dash.html import Div
+
+from ml_peg.app import APP_ROOT
+from ml_peg.app.base_app import BaseApp
+from ml_peg.app.utils.build_callbacks import (
+    plot_from_table_column,
+    struct_from_scatter,
+)
+from ml_peg.app.utils.load import read_plot
+from ml_peg.models.get_models import get_model_names
+from ml_peg.models.models import current_models
+
+MODELS = get_model_names(current_models)
+BENCHMARK_NAME = "Criegee22"
+DOCS_URL = "https://ddmms.github.io/ml-peg/user_guide/benchmarks/molecular_reactions.html#Criegee22"
+DATA_PATH = APP_ROOT / "data" / "molecular_reactions" / "Criegee22"
+
+
+class Criegee22App(BaseApp):
+    """Criegee22 benchmark app layout and callbacks."""
+
+    def register_callbacks(self) -> None:
+        """Register callbacks to app."""
+        scatter = read_plot(
+            DATA_PATH / "figure_criegee22_barriers.json",
+            id=f"{BENCHMARK_NAME}-figure",
+        )
+
+        model_dir = DATA_PATH / MODELS[0]
+        if model_dir.exists():
+            base_labels = sorted(
+                [f.stem.rsplit("_", 1)[0] for f in model_dir.glob("*_rct.xyz")]
+            )
+            structs = [
+                f"assets/molecular_reactions/Criegee22/{MODELS[0]}/{label}_rct.xyz"
+                for label in base_labels
+            ]
+        else:
+            structs = []
+
+        plot_from_table_column(
+            table_id=self.table_id,
+            plot_id=f"{BENCHMARK_NAME}-figure-placeholder",
+            column_to_plot={"MAE": scatter},
+        )
+
+        struct_from_scatter(
+            scatter_id=f"{BENCHMARK_NAME}-figure",
+            struct_id=f"{BENCHMARK_NAME}-struct-placeholder",
+            structs=structs,
+            mode="struct",
+        )
+
+
+def get_app() -> Criegee22App:
+    """
+    Get Criegee22 benchmark app layout and callback registration.
+
+    Returns
+    -------
+    Criegee22App
+        Benchmark layout and callback registration.
+    """
+    return Criegee22App(
+        name=BENCHMARK_NAME,
+        description=(
+            "Performance in predicting barrier heights for the "
+            "Criegee22 reaction benchmark. "
+            "Reference data from CCSDT(Q) calculations."
+        ),
+        docs_url=DOCS_URL,
+        table_path=DATA_PATH / "criegee22_barriers_metrics_table.json",
+        extra_components=[
+            Div(id=f"{BENCHMARK_NAME}-figure-placeholder"),
+            Div(id=f"{BENCHMARK_NAME}-struct-placeholder"),
+        ],
+    )
+
+
+if __name__ == "__main__":
+    full_app = Dash(__name__, assets_folder=DATA_PATH.parent.parent)
+    benchmark_app = get_app()
+    full_app.layout = benchmark_app.layout
+    benchmark_app.register_callbacks()
+    full_app.run(port=8068, debug=True)

ml_peg/calcs/molecular_reactions/Criegee22/calc_Criegee22.py

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+"""
+Calculate the Criegee22 dataset for reactions involving Crieegee intermediates.
+
+C. D. Smith, A. Karton. J. Comput. Chem. 2020, 41, 328–339.
+DOI: 10.1002/jcc.26106
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from ase import units
+from ase.io import read, write
+import pytest
+from tqdm import tqdm
+
+from ml_peg.calcs.utils.utils import download_s3_data
+from ml_peg.models.get_models import load_models
+from ml_peg.models.models import current_models
+
+MODELS = load_models(current_models)
+
+KJ_TO_EV = units.kJ / units.mol
+
+OUT_PATH = Path(__file__).parent / "outputs"
+
+
+@pytest.mark.parametrize("mlip", MODELS.items())
+def test_criegee22(mlip: tuple[str, Any]) -> None:
+    """
+    Run Criegee22 benchmark.
+
+    Parameters
+    ----------
+    mlip
+        Name of model use and model to get calculator.
+    """
+    model_name, model = mlip
+    calc = model.get_calculator()
+
+    data_path = (
+        download_s3_data(
+            filename="Criegee22.zip",
+            key="inputs/molecular_reactions/Criegee22/Criegee22.zip",
+        )
+        / "Criegee22"
+    )
+
+    # Read in data and attach calculator
+    calc = model.get_calculator()
+    # Add D3 calculator for this test
+    calc = model.add_d3_calculator(calc)
+
+    with open(data_path / "reference.txt") as lines:
+        # Skip header
+        next(lines)
+        for line in tqdm(lines, total=22):
+            items = line.strip().split()
+            label = items[0]
+            bh_ref = float(items[8]) * KJ_TO_EV
+            atoms_reac = read(data_path / "structures" / f"{label}-reac.xyz")
+            atoms_reac.calc = calc
+            atoms_reac.info["charge"] = 0
+            atoms_reac.info["spin"] = 1
+            atoms_reac.info["model_energy"] = atoms_reac.get_potential_energy()
+            atoms_reac.info["ref_energy"] = 0
+
+            atoms_ts = read(data_path / "structures" / f"{label}-TS.xyz")
+            atoms_ts.calc = calc
+            atoms_ts.info["charge"] = 0
+            atoms_ts.info["spin"] = 1
+            atoms_ts.info["model_energy"] = atoms_ts.get_potential_energy()
+            atoms_ts.info["ref_energy"] = bh_ref
+
+            write_dir = OUT_PATH / model_name
+            write_dir.mkdir(parents=True, exist_ok=True)
+            write(write_dir / f"{label}.xyz", [atoms_reac, atoms_ts])