add warning pre-alpha message, add equiformer-oc20

serve/app.py

@@ -1,7 +1,5 @@
 import streamlit as st
 
-
-
 # if "logged_in" not in st.session_state:
 #     st.session_state.logged_in = False
 
@@ -19,7 +17,7 @@ import streamlit as st
 # logout_page = st.Page(logout, title="Log out", icon=":material/logout:")
 
 leaderboard = st.Page(
-    "models/leaderboard.py", title="Leaderboard", icon=":material/trophy:"
+    "leaderboard.py", title="Leaderboard", icon=":material/trophy:"
 )
 # bugs = st.Page("models/bugs.py", title="Bug reports", icon=":material/bug_report:")
 # alerts = st.Page(
@@ -55,7 +53,7 @@ if pg in [stability, combustion]:
         page_icon=":shark:",
         initial_sidebar_state="expanded",
         menu_items={
-            "About": 'https://github.com/atomind-ai/mlip-arena',
+            "About": "https://github.com/atomind-ai/mlip-arena",
             "Report a bug": "https://github.com/atomind-ai/mlip-arena/issues/new",
         }
     )
@@ -66,9 +64,11 @@ else:
         page_icon=":shark:",
         initial_sidebar_state="expanded",
         menu_items={
-            "About": 'https://github.com/atomind-ai/mlip-arena',
+            "About": "https://github.com/atomind-ai/mlip-arena",
             "Report a bug": "https://github.com/atomind-ai/mlip-arena/issues/new",
         }
     )
 
-pg.run()
+st.toast("MLIP Arena is currently in **pre-alpha**. The results are not stable. Please interpret them with care. Contributions are welcome. For more information, visit https://github.com/atomind-ai/mlip-arena.", icon="🍞")
+
+pg.run()

serve/{models/leaderboard.py → leaderboard.py}

@@ -50,14 +50,17 @@ s = table.style.background_gradient(
     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.image("")
 
-# st.markdown("# MLIP Arena Leaderboard")
 
 st.dataframe(
     s,

serve/tasks/homonuclear-diatomics.py

@@ -12,7 +12,7 @@ from scipy.interpolate import CubicSpline
 from mlip_arena.models import REGISTRY
 
 st.markdown(
-"""
+    """
 # Homonuclear Diatomics
 
 Homonuclear diatomics are molecules composed of two atoms of the same element.
@@ -22,8 +22,16 @@ The potential energy curves of homonuclear diatomics are the most fundamental in
 
 st.markdown("### Methods")
 container = st.container(border=True)
-valid_models = [model for model, metadata in REGISTRY.items() if Path(__file__).stem in metadata.get("gpu-tasks", [])]
-methods = container.multiselect("MLIPs", valid_models, ["EquiformerV2(OC22)", "eSCN(OC20)", "CHGNet", "M3GNet",  "MACE-MP(M)"])
+valid_models = [
+    model
+    for model, metadata in REGISTRY.items()
+    if Path(__file__).stem in metadata.get("gpu-tasks", [])
+]
+mlip_methods = container.multiselect(
+    "MLIPs",
+    valid_models,
+    ["EquiformerV2(OC22)", "eSCN(OC20)", "CHGNet", "M3GNet", "MACE-MP(M)"],
+)
 dft_methods = container.multiselect("DFT Methods", ["GPAW"], [])
 
 st.markdown("### Settings")
@@ -34,38 +42,46 @@ ncols = vis.select_slider("Number of columns", options=[1, 2, 3, 4], value=2)
 
 # Get all attributes from pcolors.qualitative
 all_attributes = dir(pcolors.qualitative)
-color_palettes = {attr: getattr(pcolors.qualitative, attr) for attr in all_attributes if isinstance(getattr(pcolors.qualitative, attr), list)}
+color_palettes = {
+    attr: getattr(pcolors.qualitative, attr)
+    for attr in all_attributes
+    if isinstance(getattr(pcolors.qualitative, attr), list)
+}
 color_palettes.pop("__all__", None)
 
 palette_names = list(color_palettes.keys())
 palette_colors = list(color_palettes.values())
 
-palette_name = vis.selectbox(
-    "Color sequence",
-    options=palette_names, index=22
-)
+palette_name = vis.selectbox("Color sequence", options=palette_names, index=22)
 
-color_sequence = color_palettes[palette_name] # type: ignore
+color_sequence = color_palettes[palette_name]  # type: ignore
 
 DATA_DIR = Path("mlip_arena/tasks/diatomics")
-if not methods:
+if not mlip_methods and not dft_methods:
     st.stop()
-dfs = [pd.read_json(DATA_DIR / REGISTRY[method]["family"] /  "homonuclear-diatomics.json") for method in methods]
-
-dfs.extend([pd.read_json(DATA_DIR / method.lower() /  "homonuclear-diatomics.json") for method in dft_methods])
-
-
 
+dfs = [
+    pd.read_json(DATA_DIR / REGISTRY[method]["family"] / "homonuclear-diatomics.json")
+    for method in mlip_methods
+]
+dfs.extend(
+    [
+        pd.read_json(DATA_DIR / method.lower() / "homonuclear-diatomics.json")
+        for method in dft_methods
+    ]
+)
 df = pd.concat(dfs, ignore_index=True)
 df.drop_duplicates(inplace=True, subset=["name", "method"])
 
-method_color_mapping = {method: color_sequence[i % len(color_sequence)] for i, method in enumerate(df["method"].unique())}
+method_color_mapping = {
+    method: color_sequence[i % len(color_sequence)]
+    for i, method in enumerate(df["method"].unique())
+}
 
 # img_dir = Path('./images')
 # img_dir.mkdir(exist_ok=True)
 
 for i, symbol in enumerate(chemical_symbols[1:]):
-
     if i % ncols == 0:
         cols = st.columns(ncols)
 
@@ -79,6 +95,8 @@ for i, symbol in enumerate(chemical_symbols[1:]):
     elo, flo = float("inf"), float("inf")
 
     for j, method in enumerate(rows["method"].unique()):
+        if method not in mlip_methods and method not in dft_methods:
+            continue
         row = rows[rows["method"] == method].iloc[0]
 
         rs = np.array(row["R"])
@@ -101,7 +119,7 @@ for i, symbol in enumerate(chemical_symbols[1:]):
             fs = fs[ind]
 
         if "GPAW" in method:
-            xs = np.linspace(rs.min()*0.99, rs.max()*1.01, int(5e2))
+            xs = np.linspace(rs.min() * 0.99, rs.max() * 1.01, int(5e2))
         else:
             xs = rs
 
@@ -112,11 +130,12 @@ for i, symbol in enumerate(chemical_symbols[1:]):
             else:
                 ys = es
 
-            elo = min(elo, max(ys.min()*1.2, -15), -1)
+            elo = min(elo, max(ys.min() * 1.2, -15), -1)
 
             fig.add_trace(
                 go.Scatter(
-                    x=xs, y=ys,
+                    x=xs,
+                    y=ys,
                     mode="lines",
                     line=dict(
                         color=method_color_mapping[method],
@@ -130,11 +149,12 @@ for i, symbol in enumerate(chemical_symbols[1:]):
         if force_plot and "GPAW" not in method:
             ys = fs
 
-            flo = min(flo, max(ys.min()*1.2, -50))
+            flo = min(flo, max(ys.min() * 1.2, -50))
 
             fig.add_trace(
                 go.Scatter(
-                    x=xs, y=ys,
+                    x=xs,
+                    y=ys,
                     mode="lines",
                     line=dict(
                         color=method_color_mapping[method],
@@ -160,22 +180,20 @@ for i, symbol in enumerate(chemical_symbols[1:]):
 
     # Set y-axes titles
     if energy_plot:
-
         fig.update_layout(
             yaxis=dict(
                 title=dict(text="Energy [eV]"),
                 side="left",
-                range=[elo, 1.5*(abs(elo))],
+                range=[elo, 1.5 * (abs(elo))],
             )
         )
 
     if force_plot:
-
         fig.update_layout(
             yaxis2=dict(
                 title=dict(text="Force [eV/Å]"),
                 side="right",
-                range=[flo, 1.0*abs(flo)],
+                range=[flo, 1.0 * abs(flo)],
                 overlaying="y",
                 tickmode="sync",
             ),