Spaces:
Running
Running
File size: 2,572 Bytes
2bbd13c d72faca e6cac5c 5b01054 2bbd13c 5b01054 2bbd13c 3b3aaa9 5b01054 d72faca 3b3aaa9 5b01054 e6cac5c 5b01054 2bbd13c e6cac5c 2bbd13c e6cac5c 3b3aaa9 5b01054 2bbd13c d72faca 3b3aaa9 2bbd13c 9361457 3b3aaa9 af4a473 9361457 3b3aaa9 2bbd13c af4a473 5b01054 d72faca 5b01054 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
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 / method.lower() / "homonuclear-diatomics.json") for method in methods]
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).
""", unsafe_allow_html=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",
),
},
)
|