add alignn

mlip_arena/models/registry.yaml

@@ -1,8 +1,10 @@
 
 
-MACE_MP_Medium:
+MACE-MP(M):
   module: mace
+  class: MACE_MP_Medium
   username: cyrusyc # HF username
+  last-update: 2024-03-25T14:30:00
   datetime: 2024-03-25T14:30:00 # TODO: Fake datetime
   datasets: # list of training datasets
     - atomind/mptrj # TODO: fake HF dataset repo
@@ -11,6 +13,9 @@ MACE_MP_Medium:
     - qmof
   gpu-tasks:
     - diatomics
+  github: https://github.com/ACEsuit/mace
+  doi: https://arxiv.org/abs/2401.00096
+  date: 2023-12-29
 
 # CHGNet:
 #   module: chgnet

mlip_arena/models/utils.py

@@ -11,7 +11,7 @@ from mlip_arena.models import REGISTRY
 
 MLIPMap = {
     model: getattr(
-        importlib.import_module(f"{__package__}.{metadata['module']}"), model,
+        importlib.import_module(f"{__package__}.{metadata['module']}"), metadata["class"],
     )
     for model, metadata in REGISTRY.items()
 }

mlip_arena/tasks/diatomics/gpaw/homonuclear-diatomics.json

@@ -0,0 +1 @@
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AFM","R":[5.89,5.68063636,5.47127272,5.2619091,5.05254546,4.84318182,4.63381818,4.42445454,4.2150909,4.00572728,3.79636364,3.587,3.37763636,3.16827272,2.9589091,2.74954546,2.54018182,2.33081818,2.12145454,1.9120909,1.70272728,1.49336364,1.284],"E":[-3.7507724609,-3.7511695164,-3.7524752484,-3.75494705,-3.7590738891,-3.7657518923,-3.7764122983,-3.7934153989,-3.820674822,-3.8645466908,-3.9353119595,-4.049286828,-4.2312853067,-4.515307586,-4.9368928482,-5.5206742658,-6.2911676319,-7.287338808,-8.3995822955,-8.9828050516,-7.9866321912,-3.0855478518,10.6984236459],"F":[],"S^2":[]},{"name":"SS","method":"GPAW: AFM","R":[5.859,5.64,5.421,5.202,4.983,4.764,4.545,4.326,4.107,3.888,3.669,3.45,3.231,3.012,2.793,2.574,2.355,2.136,1.917,1.698,1.479,1.26],"E":[-2.0872943646,-2.0873004379,-2.0873039129,-2.0872079511,-2.0868174528,-2.0857660318,-2.083380882,-2.078425923,-2.0686482857,-2.0500147088,-2.0153841956,-1.9524021673,-2.4754979021,-2.7767417251,-3.258351891,-4.3988946521,-5.3996322157,-6.355011488,-6.8649894853,-5.9109863358,-0.9079304778,14.6482120602],"F":[],"S^2":[]},{"name":"ClCl","method":"GPAW: AFM","R":[5.642,5.43161904,5.2212381,5.01085714,4.8004762,4.59009524,4.37971428,4.16933334,3.95895238,3.74857142,3.53819048,3.32780952,3.11742858,2.90704762,2.69666666,2.48628572,2.27590476,2.0655238,1.85514286,1.6447619,1.43438096,1.224],"E":[-0.6691419821,-0.6692480928,-0.6695603479,-0.6700477101,-0.6706382596,-0.6711558026,-0.6712305447,-0.6701132644,-0.6663076419,-0.6568947846,-0.6362307457,-0.8214194441,-0.9744416064,-1.277583137,-1.8057105218,-2.4274950823,-3.0520133899,-3.4764220154,-3.2239495316,-1.1700250836,5.4597121491,23.8354114348],"F":[],"S^2":[]},{"name":"KK","method":"GPAW: AFM","R":[8.463,8.25517242,8.04734482,7.83951724,7.63168966,7.42386206,7.21603448,7.0082069,6.80037932,6.59255172,6.38472414,6.17689656,5.96906896,5.76124138,5.5534138,5.3455862,5.13775862,4.92993104,4.72210344,4.51427586,4.30644828,4.09862068,3.8907931,3.68296552,3.47513794,3.26731034,3.05948276,2.85165518,2.64382758,2.436],"E":[-0.7136104569,-0.7143245509,-0.7163897412,-0.7200179598,-0.7254714305,-0.7330796308,-0.7432196137,-0.7563032933,-0.7727696647,-0.7930729953,-0.8176566587,-0.8469153416,-0.881153594,-0.9205362812,-0.9650389401,-1.0143892442,-1.0676981173,-1.1210753671,-1.171815521,-1.2170637034,-1.2530228677,-1.2747498713,-1.2759709432,-1.248986743,-1.1847156279,-1.0727960165,-0.9011727369,-0.6533468098,-0.298395268,0.2375229275],"F":[],"S^2":[]},{"name":"ScSc","method":"GPAW: AFM","R":[7.998,7.78521428,7.57242858,7.35964286,7.14685714,6.93407142,6.72128572,6.5085,6.29571428],"E":[-1.0730187117,-1.0733920631,-1.0738206253,-1.0750916158,-1.0768513897,-1.0795146197,-1.0832385255,-1.089460395,-1.0956693732],"F":[],"S^2":[]}]

serve/app.py

@@ -47,7 +47,7 @@ pg = st.navigation(
         # "Account": [logout_page],
         # "Reports": [dashboard, bugs, alerts],
         # "Tools": [search, history, ptable],
-        "Models": [leaderboard],
+        "": [leaderboard],
         "Tasks": [diatomics],
         "Tools": [ptable],
     }

serve/models/leaderboard.py

@@ -2,115 +2,63 @@ import streamlit as st
 import pandas as pd
 from pathlib import Path
 
+from mlip_arena.models import REGISTRY
+
 DATA_DIR = Path("mlip_arena/tasks/diatomics")
-methods = ["MACE-MP", "Equiformer", "CHGNet", "MACE-OFF"]
+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", "No. of supported elements", "No. of reversed forces", "Energy-consistent forces"])
+table = pd.DataFrame(columns=[
+    "Model", 
+    "No. of supported elements",
+    "No. of reversed forces",
+    "Energy-consistent forces",
+    "Last updated",
+    "Code",
+    "Paper"
+    ])
 
 for method in df["method"].unique():
     rows = df[df["method"] == method]
+    metadata = REGISTRY.get(method, None)
     new_row = {
         "Model": method,
         "No. of 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
+        "Energy-consistent forces": None,  # Replace with actual logic if available
+        "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)
 
-
-
-# Define the data
-# data = {
-#     "Metrics": [
-#         "No. of supported elements",
-#         "No. of reversed forces",
-#         "Energy-consistent forces",
-#     ],
-#     "MACE-MP(M)": ["10", "5", "Yes"],
-#     "CHGNet": ["20", "3", "No"],
-#     "Equiformer": ["15", "7", "Yes"]
-# }
-
-# # Convert the data to a DataFrame
-# df = pd.DataFrame(data)
-
-# # Set the 'Metrics' column as the index
-# df.set_index("Metrics", inplace=True)
-
-# # Transpose the DataFrame
-# df = df.T
-
-# Apply custom CSS to center the table
-# Create the Streamlit table
-
 table.set_index("Model", inplace=True)
 
 
 s = table.style.background_gradient(
-    cmap="Spectral", 
+    cmap="PuRd", 
     subset=["No. of supported elements"],
     vmin=0, vmax=120
 )
 
 
-st.markdown("# Leaderboard")
-st.dataframe(s, use_container_width=True)
-
-# Define custom CSS for table
-# custom_css = """
-# <style>
-# table {
-#     width: 100%;
-#     border-collapse: collapse;
-# }
-# th, td {
-#     border: 1px solid #ddd;
-#     padding: 8px;
-# }
-# th {
-#     background-color: #4CAF50;
-#     color: white;
-#     text-align: left;
-# }
-# tr:nth-child(even) {
-#     background-color: #f2f2f2;
-# }
-# tr:hover {
-#     background-color: #ddd;
-# }
-# </style>
-# """
-
-# # Display the table with custom CSS
-# st.markdown(custom_css, unsafe_allow_html=True)
-# st.markdown(table.to_html(index=False), unsafe_allow_html=True)
-
-
-
-
-
-# import numpy as np
-# import plotly.figure_factory as ff
-# import streamlit as st
-
-# st.markdown("# Dashboard")
-
-# # Add histogram data
-# x1 = np.random.randn(200) - 2
-# x2 = np.random.randn(200)
-# x3 = np.random.randn(200) + 2
-
-# # Group data together
-# hist_data = [x1, x2, x3]
-
-# group_labels = ["Group 1", "Group 2", "Group 3"]
-
-# # Create distplot with custom bin_size
-# fig = ff.create_distplot(
-#     hist_data, group_labels, bin_size=[.1, .25, .5]
-# )
-
-# # Plot!
-# st.plotly_chart(fig, use_container_width=True)
+st.markdown("# MLIP Arena Leaderboard")
+
+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",
+        ),
+    },
+)

serve/tasks/homonuclear-diatomics.py

@@ -13,13 +13,13 @@ st.markdown("# Homonuclear diatomics")
 
 st.markdown("### Methods")
 container = st.container(border=True)
-methods = container.multiselect("MLIPs", ["MACE-MP", "Equiformer", "CHGNet", "MACE-OFF", "eSCN"], ["MACE-MP", "Equiformer", "CHGNet", "eSCN"])
+methods = container.multiselect("MLIPs", ["MACE-MP", "Equiformer", "CHGNet", "MACE-OFF", "eSCN", "ALIGNN"], ["MACE-MP", "Equiformer", "CHGNet", "eSCN", "ALIGNN"])
 methods += container.multiselect("DFT Methods", ["GPAW"], [])
 
 st.markdown("### Settings")
 vis = st.container(border=True)
 energy_plot = vis.checkbox("Show energy curves", value=True)
-force_plot = vis.checkbox("Show force curves", value=True)
+force_plot = vis.checkbox("Show force curves", value=False)
 ncols = vis.select_slider("Number of columns", options=[1, 2, 3, 4], value=3)
 
 # Get all attributes from pcolors.qualitative