diff --git a/ml_peg/analysis/conformers/solv_mpconf196/analyse_solv_mpconf196.py b/ml_peg/analysis/conformers/solv_mpconf196/analyse_solv_mpconf196.py
new file mode 100644
index 00000000..29998060
--- /dev/null
+++ b/ml_peg/analysis/conformers/solv_mpconf196/analyse_solv_mpconf196.py
@@ -0,0 +1,164 @@
+"""
+Analyse the solvMPCONF196 dataset of solvated biomolecule conformers.
+
+J. Comput. Chem. 2024, 45(7), 419.
+https://doi.org/10.1002/jcc.27248.
+"""
+
+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)
+
+KCAL_TO_EV = units.kcal / units.mol
+EV_TO_KCAL = 1 / KCAL_TO_EV
+CALC_PATH = CALCS_ROOT / "conformers" / "solv_mpconf196" / "outputs"
+OUT_PATH = APP_ROOT / "data" / "conformers" / "solv_mpconf196"
+
+METRICS_CONFIG_PATH = Path(__file__).with_name("metrics.yml")
+DEFAULT_THRESHOLDS, DEFAULT_TOOLTIPS, DEFAULT_WEIGHTS = load_metrics_config(
+    METRICS_CONFIG_PATH
+)
+MOLECULES = [
+    "FGG",
+    "GFA",
+    "GGF",
+    "WG",
+    "WGG",
+    "CAMVES",
+    "CHPSAR",
+    "COHVAW",
+    "GS464992",
+    "GS557577",
+    "POXTRD",
+    "SANGLI",
+    "YIVNOG",
+]
+
+
+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_solv_mpconf196.json",
+    title="Energies",
+    x_label="Predicted energy / eV",
+    y_label="Reference energy / eV",
+    hoverdata={
+        "Labels": labels(),
+    },
+)
+def conformer_energies() -> dict[str, list]:
+    """
+    Get conformer energies for all systems.
+
+    Returns
+    -------
+    dict[str, list]
+        Dictionary of all reference and predicted energies.
+    """
+    results = {"ref": []} | {mlip: [] for mlip in MODELS}
+    ref_stored = False
+
+    for model_name in MODELS:
+        for label in labels():
+            atoms = read(CALC_PATH / model_name / f"{label}.xyz")
+            results[model_name].append(atoms.info["model_rel_energy"])
+            if not ref_stored:
+                results["ref"].append(atoms.info["ref_rel_energy"])
+
+            # Write structures for app
+            structs_dir = OUT_PATH / model_name
+            structs_dir.mkdir(parents=True, exist_ok=True)
+            write(structs_dir / f"{label}.xyz", atoms)
+        ref_stored = True
+    return results
+
+
+@pytest.fixture
+def get_mae(conformer_energies) -> dict[str, float]:
+    """
+    Get mean absolute error for conformer energies.
+
+    Parameters
+    ----------
+    conformer_energies
+        Dictionary of reference and predicted conformer energies.
+
+    Returns
+    -------
+    dict[str, float]
+        Dictionary of predicted conformer energies errors for all models.
+    """
+    results = {}
+    for model_name in MODELS:
+        results[model_name] = mae(
+            conformer_energies["ref"], conformer_energies[model_name]
+        )
+    return results
+
+
+@pytest.fixture
+@build_table(
+    filename=OUT_PATH / "solv_mpconf196_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_solv_mpconf196(metrics: dict[str, dict]) -> None:
+    """
+    Run solvMPCONF196 test.
+
+    Parameters
+    ----------
+    metrics
+        All new benchmark metric names and dictionary of values for each model.
+    """
+    return
diff --git a/ml_peg/analysis/conformers/solv_mpconf196/metrics.yml b/ml_peg/analysis/conformers/solv_mpconf196/metrics.yml
new file mode 100644
index 00000000..4eb3346e
--- /dev/null
+++ b/ml_peg/analysis/conformers/solv_mpconf196/metrics.yml
@@ -0,0 +1,7 @@
+metrics:
+  MAE:
+    good: 0.0
+    bad: 1.0
+    unit: eV
+    tooltip: Mean Absolute Error for all systems
+    level_of_theory: CCSD(T)
diff --git a/ml_peg/app/conformers/solv_mpconf196/app_solv_mpconf196.py b/ml_peg/app/conformers/solv_mpconf196/app_solv_mpconf196.py
new file mode 100644
index 00000000..e387d3e2
--- /dev/null
+++ b/ml_peg/app/conformers/solv_mpconf196/app_solv_mpconf196.py
@@ -0,0 +1,90 @@
+"""Run solvMPCONF196 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 = "solvMPCONF196"
+DOCS_URL = (
+    "https://ddmms.github.io/ml-peg/user_guide/benchmarks/molecular.html#solvmpconf196"
+)
+DATA_PATH = APP_ROOT / "data" / "conformers" / "solv_mpconf196"
+
+
+class SolvMPCONF196App(BaseApp):
+    """SolvMPCONF196 benchmark app layout and callbacks."""
+
+    def register_callbacks(self) -> None:
+        """Register callbacks to app."""
+        scatter = read_plot(
+            DATA_PATH / "figure_solv_mpconf196.json",
+            id=f"{BENCHMARK_NAME}-figure",
+        )
+
+        model_dir = DATA_PATH / MODELS[0]
+        if model_dir.exists():
+            labels = sorted([f.stem for f in model_dir.glob("*.xyz")])
+            structs = [
+                f"assets/conformers/solv_mpconf196/{MODELS[0]}/{label}.xyz"
+                for label in 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() -> SolvMPCONF196App:
+    """
+    Get solvMPCONF196 benchmark app layout and callback registration.
+
+    Returns
+    -------
+    SolvMPCONF196App
+        Benchmark layout and callback registration.
+    """
+    return SolvMPCONF196App(
+        name=BENCHMARK_NAME,
+        description=(
+            "Performance in predicting solvent-stabilized conformer energies for "
+            "the solvMPCONF196 dataset (13 biomolecular fragments with explicit "
+            "solvation). Reference data from CCSD(T) calculations."
+        ),
+        docs_url=DOCS_URL,
+        table_path=DATA_PATH / "solv_mpconf196_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=8069, debug=True)
diff --git a/ml_peg/calcs/conformers/solv_mpconf196/.dvc/.gitignore b/ml_peg/calcs/conformers/solv_mpconf196/.dvc/.gitignore
new file mode 100644
index 00000000..528f30c7
--- /dev/null
+++ b/ml_peg/calcs/conformers/solv_mpconf196/.dvc/.gitignore
@@ -0,0 +1,3 @@
+/config.local
+/tmp
+/cache
diff --git a/ml_peg/calcs/conformers/solv_mpconf196/.dvc/config b/ml_peg/calcs/conformers/solv_mpconf196/.dvc/config
new file mode 100644
index 00000000..e69de29b
diff --git a/ml_peg/calcs/conformers/solv_mpconf196/.dvcignore b/ml_peg/calcs/conformers/solv_mpconf196/.dvcignore
new file mode 100644
index 00000000..51973055
--- /dev/null
+++ b/ml_peg/calcs/conformers/solv_mpconf196/.dvcignore
@@ -0,0 +1,3 @@
+# Add patterns of files dvc should ignore, which could improve
+# the performance. Learn more at
+# https://dvc.org/doc/user-guide/dvcignore
diff --git a/ml_peg/calcs/conformers/solv_mpconf196/calc_solv_mpconf196.py b/ml_peg/calcs/conformers/solv_mpconf196/calc_solv_mpconf196.py
new file mode 100644
index 00000000..c087bc81
--- /dev/null
+++ b/ml_peg/calcs/conformers/solv_mpconf196/calc_solv_mpconf196.py
@@ -0,0 +1,191 @@
+"""
+Calculate the solvMPCONF196 dataset of solvated biomolecule conformers.
+
+J. Comput. Chem. 2024, 45(7), 419.
+https://doi.org/10.1002/jcc.27248.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from ase import units
+from ase.io import read, write
+import mlipx
+from mlipx.abc import NodeWithCalculator
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+import zntrack
+
+from ml_peg.calcs.utils.utils import chdir, 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)
+
+KCAL_TO_EV = units.kcal / units.mol
+EV_TO_KCAL = 1 / KCAL_TO_EV
+
+OUT_PATH = Path(__file__).parent / "outputs"
+
+MOLECULES = [
+    "FGG",
+    "GFA",
+    "GGF",
+    "WG",
+    "WGG",
+    "CAMVES",
+    "CHPSAR",
+    "COHVAW",
+    "GS464992",
+    "GS557577",
+    "POXTRD",
+    "SANGLI",
+    "YIVNOG",
+]
+
+
+class SolvMPCONF196Benchmark(zntrack.Node):
+    """Benchmark the solvMPCONF196 dataset."""
+
+    model: NodeWithCalculator = zntrack.deps()
+    model_name: str = zntrack.params()
+
+    @staticmethod
+    def get_atoms(atoms_path):
+        """
+        Read atoms object and add charge and spin.
+
+        Parameters
+        ----------
+        atoms_path
+            Path to atoms object.
+
+        Returns
+        -------
+        atoms
+            ASE atoms object.
+        """
+        atoms = read(atoms_path)
+        atoms.info["charge"] = 0
+        atoms.info["spin"] = 1
+        return atoms
+
+    def get_ref_energies(self, data_path):
+        """
+        Get reference conformer energies.
+
+        Parameters
+        ----------
+        data_path
+            Path to the structure.
+        """
+        df = pd.read_excel(
+            data_path / "Energies_CCSD(T).xlsx",
+            sheet_name="Total PNO-CCS(T) Energies solvM",
+            header=1,
+        )
+        self.ref_energies = {}
+        for row in df.iterrows():
+            label = row[1][0]
+            e_ref = float(row[1][1]) * units.Hartree
+            self.ref_energies[label] = e_ref
+
+    def run(self):
+        """Run new benchmark."""
+        data_path = (
+            download_s3_data(
+                filename="SolvMPCONF196.zip",
+                key="inputs/conformers/SolvMPCONF196/SolvMPCONF196.zip",
+            )
+            / "SolvMPCONF196"
+        )
+        self.get_ref_energies(data_path)
+        # Read in data and attach calculator
+        calc = self.model.get_calculator()
+        # Add D3 calculator for this test
+        calc = self.model.add_d3_calculator(calc)
+        for molecule in tqdm(MOLECULES):
+            model_abs_energies = []
+            ref_abs_energies = []
+            current_molecule_labels = []
+            for label, e_ref in self.ref_energies.items():
+                molecule_label = label.split("_")[0]
+                conformer_label = label.split("_")[1]
+                if label[-1].isnumeric():
+                    xyz_label = f"{molecule_label}{conformer_label}"
+                else:
+                    xyz_label = f"{molecule_label}_{conformer_label}"
+                if molecule != molecule_label:
+                    continue
+                atoms = self.get_atoms(
+                    data_path
+                    / "solvMPCONF196_geometries/solvMPCONF196"
+                    / xyz_label
+                    / "struc.xyz"
+                )
+                atoms.translate(-atoms.get_center_of_mass())
+                atoms.calc = calc
+                model_abs_energies.append(atoms.get_potential_energy())
+                ref_abs_energies.append(e_ref)
+                current_molecule_labels.append(label)
+
+            for label, e_model in zip(
+                current_molecule_labels, model_abs_energies, strict=False
+            ):
+                molecule_label = label.split("_")[0]
+                conformer_label = label.split("_")[1]
+                if label[-1].isnumeric():
+                    xyz_label = f"{molecule_label}{conformer_label}"
+                else:
+                    xyz_label = f"{molecule_label}_{conformer_label}"
+                if molecule != molecule_label:
+                    continue
+                atoms = self.get_atoms(
+                    data_path
+                    / "solvMPCONF196_geometries/solvMPCONF196"
+                    / xyz_label
+                    / "struc.xyz"
+                )
+                atoms.translate(-atoms.get_center_of_mass())
+                atoms.info["ref_rel_energy"] = self.ref_energies[label] - np.mean(
+                    ref_abs_energies
+                )
+                atoms.info["model_rel_energy"] = e_model - np.mean(model_abs_energies)
+
+                write_dir = OUT_PATH / self.model_name
+                write_dir.mkdir(parents=True, exist_ok=True)
+                write(write_dir / f"{label}.xyz", atoms)
+
+
+def build_project(repro: bool = False) -> None:
+    """
+    Build mlipx project.
+
+    Parameters
+    ----------
+    repro
+        Whether to call dvc repro -f after building.
+    """
+    project = mlipx.Project()
+    benchmark_node_dict = {}
+
+    for model_name, model in MODELS.items():
+        with project.group(model_name):
+            benchmark = SolvMPCONF196Benchmark(
+                model=model,
+                model_name=model_name,
+            )
+            benchmark_node_dict[model_name] = benchmark
+
+    if repro:
+        with chdir(Path(__file__).parent):
+            project.repro(build=True, force=True)
+    else:
+        project.build()
+
+
+def test_solv_mpconf196():
+    """Run solvMPCONF196 benchmark via pytest."""
+    build_project(repro=True)