serve/leaderboard.py

from pathlib import Path

import pandas as pd
import streamlit as st

# from mlip_arena.models.utils import MLIPEnum, REGISTRY
from mlip_arena.models import REGISTRY

DATA_DIR = Path("mlip_arena/tasks/diatomics")
# methods = ["MACE-MP", "Equiformer", "CHGNet", "MACE-OFF", "eSCN", "ALIGNN"]

dfs = [pd.read_json(DATA_DIR / REGISTRY[model].get("family") /  "homonuclear-diatomics.json") for model in REGISTRY]
df = pd.concat(dfs, ignore_index=True)


table = pd.DataFrame(columns=[
    "Model",
    "Supported elements",
    # "No. of reversed forces",
    # "Energy-consistent forces",
    "Prediction",
    "NVT",
    "NPT",
    "Code",
    "Paper",
    "Last updated",
    ])

for model in REGISTRY:
    rows = df[df["method"] == model]
    metadata = REGISTRY.get(model, {})
    new_row = {
        "Model": model,
        "Supported elements": len(rows["name"].unique()),
        # "No. of reversed forces": None,  # Replace with actual logic if available
        # "Energy-consistent forces": None,  # Replace with actual logic if available
        "Prediction": metadata.get("prediction", None),
        "NVT": "✅" if metadata.get("nvt", False) else "❌",
        "NPT": "✅" if metadata.get("npt", False) else "❌",
        "Code": metadata.get("github", None) if metadata else None,
        "Paper": metadata.get("doi", None) if metadata else None,
    }
    table = pd.concat([table, pd.DataFrame([new_row])], ignore_index=True)

table.set_index("Model", inplace=True)


s = table.style.background_gradient(
    cmap="PuRd",
    subset=["Supported elements"],
    vmin=0, vmax=120
)

st.warning("MLIP Arena is currently in **pre-alpha**. The results are not stable. Please interpret them with care.", icon="⚠️")
st.info("Contributions are welcome. For more information, visit https://github.com/atomind-ai/mlip-arena.", icon="🤗")

st.markdown(
"""
<h1 style='text-align: center;'>⚔️ MLIP Arena Leaderboard ⚔️</h1>

MLIP Arena is a platform for benchmarking foundation machine learning interatomic potentials (MLIPs), mainly for disclosing the learned physics and chemistry of the models and their performance on molecular dynamics (MD) simulations.
The benchmarks are designed to evaluate the readiness and reliability of open-source, open-weight models to reproduce the qualitatively or quantitatively correct physics.
""", unsafe_allow_html=True)

st.header("Summary", divider=True)

st.dataframe(
    s,
    use_container_width=True,
    column_config={
        "Code": st.column_config.LinkColumn(
            # "GitHub",
            # help="The top trending Streamlit apps",
            # validate="^https://[a-z]+\.streamlit\.app$",
            max_chars=100,
            display_text="GitHub",
        ),
        "Paper": st.column_config.LinkColumn(
            # validate="^https://[a-z]+\.streamlit\.app$",
            max_chars=100,
            display_text="arXiv",
        ),
    },
)