improve stability visualization; update leaderborad

mlip_arena/models/__init__.py

@@ -1,4 +1,5 @@
-import os
+from __future__ import annotations
+
 from pathlib import Path
 
 import torch
@@ -10,7 +11,7 @@ from torch import nn
 
 # from torch_geometric.data import Data
 
-with open(Path(__file__).parent / "registry.yaml") as f:
+with open(Path(__file__).parent / "registry.yaml", encoding="utf-8") as f:
     REGISTRY = yaml.safe_load(f)
 
 

mlip_arena/models/externals.py

@@ -74,6 +74,34 @@ class MACE_MP_Medium(MACECalculator):
             model_paths=model, device=device, default_dtype=default_dtype, **kwargs
         )
 
+class MACE_OFF_Medium(MACECalculator):
+    def __init__(self, device=None, default_dtype="float32", **kwargs):
+        checkpoint_url = "https://github.com/ACEsuit/mace-off/raw/main/mace_off23/MACE-OFF23_medium.model?raw=true"
+        cache_dir = os.path.expanduser("~/.cache/mace")
+        checkpoint_url_name = "".join(
+            c for c in os.path.basename(checkpoint_url) if c.isalnum() or c in "_"
+        )
+        cached_model_path = f"{cache_dir}/{checkpoint_url_name}"
+        if not os.path.isfile(cached_model_path):
+            os.makedirs(cache_dir, exist_ok=True)
+            # download and save to disk
+            print(f"Downloading MACE model from {checkpoint_url!r}")
+            _, http_msg = urllib.request.urlretrieve(checkpoint_url, cached_model_path)
+            if "Content-Type: text/html" in http_msg:
+                raise RuntimeError(
+                    f"Model download failed, please check the URL {checkpoint_url}"
+                )
+            print(f"Cached MACE model to {cached_model_path}")
+        model = cached_model_path
+        msg = f"Using Materials Project MACE for MACECalculator with {model}"
+        print(msg)
+
+        device = device or str(get_freer_device())
+
+        super().__init__(
+            model_paths=model, device=device, default_dtype=default_dtype, **kwargs
+        )
+
 
 class CHGNet(CHGNetCalculator):
     def __init__(

mlip_arena/models/registry.yaml

@@ -1,22 +1,24 @@
-
-
 MACE-MP(M):
   module: externals
   class: MACE_MP_Medium
   family: mace-mp
-  username: cyrusyc # HF username
+  username: cyrusyc
   last-update: 2024-03-25T14:30:00
   datetime: 2024-03-25T14:30:00 # TODO: Fake datetime
-  datasets: # list of training datasets
+  datasets:
     - atomind/mptrj # TODO: fake HF dataset repo
   cpu-tasks:
     - alexandria
     - qmof
   gpu-tasks:
-    - diatomics
+    - homonuclear-diatomics
+    - stability
   github: https://github.com/ACEsuit/mace
   doi: https://arxiv.org/abs/2401.00096
   date: 2023-12-29
+  prediction: EFS
+  nvt: true
+  npt: true
 
 CHGNet:
   module: externals
@@ -28,7 +30,11 @@ CHGNet:
   datasets:
     - atomind/mptrj
   gpu-tasks:
-    - diatomics
+    - homonuclear-diatomics
+    - stability
+  prediction: EFSM
+  nvt: true
+  npt: true
 
 EquiformerV2(OC22):
   module: externals
@@ -40,7 +46,12 @@ EquiformerV2(OC22):
   datasets:
     - ocp
   gpu-tasks:
-    - diatomics
+    - homonuclear-diatomics
+  github: https://github.com/FAIR-Chem/fairchem
+  doi: https://arxiv.org/abs/2306.12059
+  prediction: EF
+  nvt: true
+  npt: false
 
 eSCN(OC20):
   module: externals
@@ -52,14 +63,45 @@ eSCN(OC20):
   datasets:
     - ocp
   gpu-tasks:
-    - diatomics
+    - homonuclear-diatomics
+  github: https://github.com/FAIR-Chem/fairchem
+  doi: https://arxiv.org/abs/2302.03655
+  prediction: EF
+  nvt: true
+  npt: false
 
+MACE-OFF(M):
+  module: externals
+  class: MACE_OFF_Medium
+  family: mace-off
+  username: cyrusyc
+  last-update: 2024-03-25T14:30:00
+  datetime: 2024-03-25T14:30:00 # TODO: Fake datetime
+  datasets:
+    - atomind/mptrj # TODO: fake HF dataset repo
+  cpu-tasks:
+    - alexandria
+    - qmof
+  gpu-tasks:
+    - homonuclear-diatomics
+  github: https://github.com/ACEsuit/mace
+  doi: https://arxiv.org/abs/2312.15211
+  date: 2023-12-29
+  prediction: EFS
+  nvt: true
+  npt: true
 
-# CHGNet:
-#   module: chgnet
-#   username: cyrusyc
-#   datetime: 2024-03-25T14:30:00
-#   datasets:
-#     - atomind/mptrj
-#   cpu-tasks:
-#     - diatomics
+ALIGNN:
+  module: externals
+  class: ALIGNN
+  family: alignn
+  username: cyrusyc
+  last-update: 2024-07-08T00:00:00
+  datetime: 2024-07-08T00:00:00
+  datasets:
+    - atomind/mptrj
+  gpu-tasks:
+    - homonuclear-diatomics
+  prediction: EFS
+  nvt: true
+  npt: true

mlip_arena/tasks/__init__.py

@@ -1,4 +1,3 @@
-import os
 from pathlib import Path
 
 import yaml
@@ -7,6 +6,10 @@ from huggingface_hub import HfApi, HfFileSystem, hf_hub_download
 from mlip_arena.models import MLIP
 from mlip_arena.models import REGISTRY as MODEL_REGISTRY
 
+from .run import md as MD
+
+__all__ = ["MD"]
+
 with open(Path(__file__).parent / "registry.yaml") as f:
     REGISTRY = yaml.safe_load(f)
 

mlip_arena/tasks/diatomics/mlip.py

@@ -1,165 +0,0 @@
-from datetime import timedelta
-from typing import Union
-
-# import covalent as ct
-import numpy as np
-import pandas as pd
-import torch
-from ase import Atoms
-from ase.calculators.calculator import Calculator
-from ase.data import chemical_symbols
-from dask.distributed import Client
-from dask_jobqueue import SLURMCluster
-from prefect import flow, task
-from prefect.tasks import task_input_hash
-from prefect_dask import DaskTaskRunner
-
-from mlip_arena.models import MLIPCalculator
-from mlip_arena.models.utils import EXTMLIPEnum, MLIPMap, external_ase_calculator
-
-cluster_kwargs = {
-    "cores": 4,
-    "memory": "64 GB",
-    "shebang": "#!/bin/bash",
-    "account": "m3828",
-    "walltime": "00:10:00",
-    "job_mem": "0",
-    "job_script_prologue": ["source ~/.bashrc"],
-    "job_directives_skip": ["-n", "--cpus-per-task"],
-    "job_extra_directives": ["-q debug", "-C gpu"],
-}
-
-cluster = SLURMCluster(**cluster_kwargs)
-cluster.scale(jobs=10)
-client = Client(cluster)
-
-
-@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(hours=1))
-def calculate_single_diatomic(
-    calculator_name: str | EXTMLIPEnum,
-    calculator_kwargs: dict | None,
-    atom1: str,
-    atom2: str,
-    rmin: float = 0.1,
-    rmax: float = 6.5,
-    npts: int = int(1e3),
-):
-
-    calculator_kwargs = calculator_kwargs or {}
-
-    if isinstance(calculator_name, EXTMLIPEnum) and calculator_name in EXTMLIPEnum:
-        calc = external_ase_calculator(calculator_name, **calculator_kwargs)
-    elif calculator_name in MLIPMap:
-        calc = MLIPMap[calculator_name](**calculator_kwargs)
-
-    a = 2 * rmax
-
-    rs = np.linspace(rmin, rmax, npts)
-    e = np.zeros_like(rs)
-    f = np.zeros_like(rs)
-
-    da = atom1 + atom2
-
-    for i, r in enumerate(rs):
-
-        positions = [
-            [0, 0, 0],
-            [r, 0, 0],
-        ]
-
-        # Create the unit cell with two atoms
-        atoms = Atoms(da, positions=positions, cell=[a, a, a])
-
-        atoms.calc = calc
-
-        e[i] = atoms.get_potential_energy()
-        f[i] = np.inner(np.array([1, 0, 0]), atoms.get_forces()[1])
-
-    return {"r": rs, "E": e, "F": f, "da": da}
-
-
-@flow
-def calculate_multiple_diatomics(calculator_name, calculator_kwargs):
-
-    futures = []
-    for symbol in chemical_symbols:
-        if symbol == "X":
-            continue
-        futures.append(
-            calculate_single_diatomic.submit(
-                calculator_name, calculator_kwargs, symbol, symbol
-            )
-        )
-
-    return [i for future in futures for i in future.result()]
-
-
-@flow(task_runner=DaskTaskRunner(address=client.scheduler.address), log_prints=True)
-def calculate_homonuclear_diatomics(calculator_name, calculator_kwargs):
-
-    curves = calculate_multiple_diatomics(calculator_name, calculator_kwargs)
-
-    pd.DataFrame(curves).to_csv(f"homonuclear-diatomics-{calculator_name}.csv")
-
-
-# with plt.style.context("default"):
-
-#     SMALL_SIZE = 6
-#     MEDIUM_SIZE = 8
-#     LARGE_SIZE = 10
-
-#     LINE_WIDTH = 1
-
-#     plt.rcParams.update(
-#         {
-#             "pgf.texsystem": "pdflatex",
-#             "font.family": "sans-serif",
-#             "text.usetex": True,
-#             "pgf.rcfonts": True,
-#             "figure.constrained_layout.use": True,
-#             "axes.labelsize": MEDIUM_SIZE,
-#             "axes.titlesize": MEDIUM_SIZE,
-#             "legend.frameon": False,
-#             "legend.fontsize": MEDIUM_SIZE,
-#             "legend.loc": "best",
-#             "lines.linewidth": LINE_WIDTH,
-#             "xtick.labelsize": SMALL_SIZE,
-#             "ytick.labelsize": SMALL_SIZE,
-#         }
-#     )
-
-#     fig, ax = plt.subplots(layout="constrained", figsize=(3, 2), dpi=300)
-
-#     color = "tab:red"
-#     ax.plot(rs, e, color=color, zorder=1)
-
-#     ax.axhline(ls="--", color=color, alpha=0.5, lw=0.5 * LINE_WIDTH)
-
-#     ylo, yhi = ax.get_ylim()
-#     ax.set(xlabel=r"r [$\AA]$", ylim=(max(-7, ylo), min(5, yhi)))
-#     ax.set_ylabel(ylabel="E [eV]", color=color)
-#     ax.tick_params(axis="y", labelcolor=color)
-#     ax.text(0.8, 0.85, da, fontsize=LARGE_SIZE, transform=ax.transAxes)
-
-#     color = "tab:blue"
-
-#     at = ax.twinx()
-#     at.plot(rs, f, color=color, zorder=0, lw=0.5 * LINE_WIDTH)
-
-#     at.axhline(ls="--", color=color, alpha=0.5, lw=0.5 * LINE_WIDTH)
-
-#     ylo, yhi = at.get_ylim()
-#     at.set(
-#         xlabel=r"r [$\AA]$",
-#         ylim=(max(-20, ylo), min(20, yhi)),
-#     )
-#     at.set_ylabel(ylabel="F [eV/$\AA$]", color=color)
-#     at.tick_params(axis="y", labelcolor=color)
-
-#     plt.show()
-
-
-if __name__ == "__main__":
-    calculate_homonuclear_diatomics(
-        EXTMLIPEnum.MACE, dict(model="medium", device="cuda")
-    )

mlip_arena/tasks/stability/chgnet/chloride-salts.json

@@ -1 +1 @@
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mlip_arena/tasks/stability/run.py

@@ -244,8 +244,8 @@ def md(
 
         if restart and traj_file.exists():
             traj = read(traj_file, index=":")
-            last_step = len(traj)
-            n_steps -= len(traj)
+            last_step = traj[-1].info.get("step", len(traj) * traj_interval)
+            n_steps -= last_step
             last_atoms = traj[-1]
             traj = Trajectory(traj_file, "a", atoms)
             atoms.set_positions(last_atoms.get_positions())

requirements.txt

@@ -7,3 +7,5 @@ torch==2.2.1
 pymatgen==2024.4.13
 bokeh==2.4.3
 statsmodels==0.14.2
+# py3Dmol==2.0.0.post2
+# stmol==0.0.9

serve/app.py

@@ -1,15 +1,6 @@
 import streamlit as st
 
-st.set_page_config(
-    layout="wide",
-    page_title="MLIP Arena",
-    page_icon=":shark:",
-    initial_sidebar_state="expanded",
-    menu_items={
-        "About": 'https://github.com/atomind-ai/mlip-arena',
-        "Report a bug": "https://github.com/atomind-ai/mlip-arena/issues/new",
-    }
-)
+
 
 # if "logged_in" not in st.session_state:
 #     st.session_state.logged_in = False
@@ -39,8 +30,8 @@ search = st.Page("tools/search.py", title="Search", icon=":material/search:")
 history = st.Page("tools/history.py", title="History", icon=":material/history:")
 ptable = st.Page("tools/ptable.py", title="Periodic table", icon=":material/gradient:")
 
-diatomics = st.Page("tasks/homonuclear-diatomics.py", title="Homonuclear diatomics", icon=":material/target:", default=True)
-stability = st.Page("tasks/stability.py", title="Stability", icon=":material/target:")
+diatomics = st.Page("tasks/homonuclear-diatomics.py", title="Homonuclear Diatomics", icon=":material/target:", default=True)
+stability = st.Page("tasks/stability.py", title="High Pressure Stability", icon=":material/target:")
 
 
 # if st.session_state.logged_in:
@@ -57,4 +48,27 @@ pg = st.navigation(
 # else:
 #     pg = st.navigation([login_page])
 
+if pg in [stability]:
+    st.set_page_config(
+        layout="centered",
+        page_title="MLIP Arena",
+        page_icon=":shark:",
+        initial_sidebar_state="expanded",
+        menu_items={
+            "About": 'https://github.com/atomind-ai/mlip-arena',
+            "Report a bug": "https://github.com/atomind-ai/mlip-arena/issues/new",
+        }
+    )
+else:
+    st.set_page_config(
+        layout="wide",
+        page_title="MLIP Arena",
+        page_icon=":shark:",
+        initial_sidebar_state="expanded",
+        menu_items={
+            "About": 'https://github.com/atomind-ai/mlip-arena',
+            "Report a bug": "https://github.com/atomind-ai/mlip-arena/issues/new",
+        }
+    )
+
 pg.run()

serve/models/leaderboard.py

@@ -3,31 +3,38 @@ from pathlib import Path
 import pandas as pd
 import streamlit as st
 
-from mlip_arena.models import REGISTRY
+from mlip_arena.models.utils import MLIPEnum, 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",
-    "No. of supported elements",
-    "No. of reversed forces",
-    "Energy-consistent forces",
+    "Supported elements",
+    # "No. of reversed forces",
+    # "Energy-consistent forces",
+    "Prediction",
+    "NVT",
+    "NPT",
     "Last updated",
     "Code",
     "Paper"
     ])
 
-for method in df["method"].unique():
-    rows = df[df["method"] == method]
-    metadata = REGISTRY.get(method, None)
+for model in MLIPEnum:
+    rows = df[df["method"] == model.name]
+    metadata = REGISTRY.get(model.name, {})
     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
+        "Model": model.name,
+        "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,
     }
@@ -38,12 +45,16 @@ table.set_index("Model", inplace=True)
 
 s = table.style.background_gradient(
     cmap="PuRd",
-    subset=["No. of supported elements"],
+    subset=["Supported elements"],
     vmin=0, vmax=120
 )
 
+st.markdown(
+"""
+<h1 style='text-align: center;'>MLIP Arena Leaderboard</h1>
+""", unsafe_allow_html=True)
 
-st.markdown("# MLIP Arena Leaderboard")
+# st.markdown("# MLIP Arena Leaderboard")
 
 st.dataframe(
     s,

serve/tasks/homonuclear-diatomics.py

@@ -9,11 +9,11 @@ from ase.data import chemical_symbols
 from plotly.subplots import make_subplots
 from scipy.interpolate import CubicSpline
 
-# from mlip_arena.models.utils import MLIPMap
+from mlip_arena.models.utils import REGISTRY
 
 st.markdown(
 """
-# Homonuclear diatomics
+# Homonuclear Diatomics
 
 Homonuclear diatomics are molecules composed of two atoms of the same element.
 The potential energy curves of homonuclear diatomics are the most fundamental interactions between atoms in quantum chemistry.
@@ -22,8 +22,9 @@ The potential energy curves of homonuclear diatomics are the most fundamental in
 
 st.markdown("### Methods")
 container = st.container(border=True)
-methods = container.multiselect("MLIPs", ["MACE-MP", "Equiformer", "CHGNet", "MACE-OFF", "eSCN", "ALIGNN"], ["MACE-MP", "Equiformer", "CHGNet", "eSCN", "ALIGNN"])
-methods += container.multiselect("DFT Methods", ["GPAW"], [])
+valid_models = [model for model, metadata in REGISTRY.items() if Path(__file__).stem in metadata.get("gpu-tasks", [])]
+methods = container.multiselect("MLIPs", valid_models, ["MACE-MP(M)", "EquiformerV2(OC22)", "CHGNet", "eSCN(OC20)", "ALIGNN"])
+dft_methods = container.multiselect("DFT Methods", ["GPAW"], [])
 
 st.markdown("### Settings")
 vis = st.container(border=True)
@@ -49,7 +50,12 @@ color_sequence = color_palettes[palette_name] # type: ignore
 DATA_DIR = Path("mlip_arena/tasks/diatomics")
 if not methods:
     st.stop()
-dfs = [pd.read_json(DATA_DIR / method.lower() /  "homonuclear-diatomics.json") for method in methods]
+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])
+
+
+
 df = pd.concat(dfs, ignore_index=True)
 df.drop_duplicates(inplace=True, subset=["name", "method"])
 
@@ -111,7 +117,7 @@ for i, symbol in enumerate(chemical_symbols[1:]):
                     mode="lines",
                     line=dict(
                         color=method_color_mapping[method],
-                        width=2,
+                        width=3,
                     ),
                     name=method,
                 ),
@@ -129,8 +135,8 @@ for i, symbol in enumerate(chemical_symbols[1:]):
                     mode="lines",
                     line=dict(
                         color=method_color_mapping[method],
-                        width=1,
-                        dash="dot",
+                        width=2,
+                        dash="dashdot",
                     ),
                     name=method,
                     showlegend=not energy_plot,

serve/tasks/stability.py

@@ -6,22 +6,30 @@ import plotly.colors as pcolors
 import plotly.express as px
 import plotly.graph_objects as go
 import streamlit as st
+from scipy.optimize import curve_fit
 
 from mlip_arena.models import REGISTRY
 
 DATA_DIR = Path("mlip_arena/tasks/stability")
 
 
-st.markdown(
-    """
-# Stability
+st.markdown("""
+# High Pressure Stability
 
-"""
-)
+Stable and accurate molecular dynamics (MD) simulations are important for understanding the properties of matters.
+However, many MLIPs have unphysical potential energy surface (PES) at the short-range interatomic distances or 
+under many-body effect. These are often manifested as softened repulsion and hole in the PES and can lead to incorrect 
+and sampling of the phase space.
+
+Here, we analyze the stability of the MD simulations under high pressure conditions by gradually increasing the pressure 
+from 0 to 100 GPa until the system crashes or completes 100 ps steps.
+""")
 
 st.markdown("### Methods")
 container = st.container(border=True)
-models = container.multiselect("MLIPs", REGISTRY.keys(), ["MACE-MP(M)", "CHGNet"])
+valid_models = [model for model, metadata in REGISTRY.items() if Path(__file__).stem in metadata.get("gpu-tasks", [])]
+
+models = container.multiselect("MLIPs", valid_models, ["MACE-MP(M)", "CHGNet"])
 
 st.markdown("### Settings")
 vis = st.container(border=True)
@@ -58,23 +66,6 @@ method_color_mapping = {
     for i, method in enumerate(df["method"].unique())
 }
 
-
-fig = px.scatter(
-    df,
-    x="natoms",
-    y="steps_per_second",
-    color="method",
-    size="total_steps",
-    hover_data=["material_id", "formula"],
-    color_discrete_map=method_color_mapping,
-    trendline="ols",
-    trendline_options=dict(log_x=True),
-    log_x=True,
-    title="Inference Speed",
-    labels={"steps_per_second": "Steps per second", "natoms": "Number of atoms"},
-)
-st.plotly_chart(fig)
-
 ###
 
 fig = go.Figure()
@@ -83,23 +74,29 @@ fig = go.Figure()
 # bins = np.histogram_bin_edges(df['total_steps'], bins=10)
 
 max_steps = df["total_steps"].max()
+max_target_steps = df["target_steps"].max()
 
-bins = np.append(np.arange(0, max_steps - 1, max_steps // 10), max_steps)
+bins = np.append(np.arange(0, max_steps + 1, max_steps // 10), max_target_steps)
 bin_labels = [f"{bins[i]}-{bins[i+1]}" for i in range(len(bins)-1)]
 
 num_bins = len(bin_labels)
-colormap = px.colors.sequential.Redor
+colormap = px.colors.sequential.Darkmint_r
 indices = np.linspace(0, len(colormap) - 1, num_bins, dtype=int)
 bin_colors = [colormap[i] for i in indices]
+# bin_colors[-1] = px.colors.sequential.Greens[-1]
 
 # Initialize a dictionary to hold the counts for each method and bin range
-counts_per_method = {method: [0] * len(bin_labels) for method in df['method'].unique()}
+# counts_per_method = {method: [0] * len(bin_labels) for method in df["method"].unique()}
+counts_per_method = {method: [0] * len(bin_labels) for method in df["method"].unique()}
+
 
 # Populate the dictionary with counts
-for method, group in df.groupby('method'):
-    counts, _ = np.histogram(group['total_steps'], bins=bins)
+for method, group in df.groupby("method"):
+    counts, _ = np.histogram(group["total_steps"], bins=bins)
     counts_per_method[method] = counts
 
+
+count_or_percetange = st.toggle("show counts", False)
 # Create a figure
 fig = go.Figure()
 
@@ -108,23 +105,23 @@ for i, bin_label in enumerate(bin_labels):
     for method, counts in counts_per_method.items():
         fig.add_trace(go.Bar(
             # name=method,  # This will be the legend entry
-            x=[counts[i]],  # Count for this bin
+            x=[counts[i]/counts.sum()*100] if not count_or_percetange else [counts[i]],
             y=[method],  # Method as the y-axis category
             # name=bin_label,
-            orientation='h',  # Horizontal bars
+            orientation="h",  # Horizontal bars
             marker=dict(
                 color=bin_colors[i],
-                line=dict(color='rgb(248, 248, 249)', width=1)
+                line=dict(color="rgb(248, 248, 249)", width=1)
             ),
-            text=bin_label,
+            text=f"{bin_label}: {counts[i]/counts.sum()*100:.0f}%",
             width=0.5
         ))
 
 # Update the layout to stack the bars
 fig.update_layout(
-    barmode='stack',  # Stack the bars
-    title="Total MD Steps",
-    xaxis_title="Count",
+    barmode="stack",  # Stack the bars
+    title="Total MD steps (before crash or completion)",
+    xaxis_title="Percentage (%)" if not count_or_percetange else "Count",
     yaxis_title="Method",
     showlegend=False
 )
@@ -156,3 +153,53 @@ fig.update_layout(
 # )
 
 st.plotly_chart(fig)
+
+
+###
+
+# st.markdown("""
+# ## Runtime Analysis
+
+# """)
+
+fig = px.scatter(
+    df,
+    x="natoms",
+    y="steps_per_second",
+    color="method",
+    size="total_steps",
+    hover_data=["material_id", "formula"],
+    color_discrete_map=method_color_mapping,
+    # trendline="ols",
+    # trendline_options=dict(log_x=True),
+    log_x=True,
+    # log_y=True,
+    # range_y=[1, 1e2],
+    range_x=[df["natoms"].min()*0.9, df["natoms"].max()*1.1],
+    # range_x=[1e3, 1e2],
+    title="Inference speed (on single A100 GPU)",
+    labels={"steps_per_second": "Steps per second", "natoms": "Number of atoms"},
+)
+
+
+def func(x, a, n):
+    return a * x ** (-n)
+
+x = np.linspace(df["natoms"].min(), df["natoms"].max(), 100)
+
+for method, data in df.groupby("method"):
+    data.dropna(subset=["steps_per_second"], inplace=True)
+    popt, pcov = curve_fit(func, data["natoms"], data["steps_per_second"])
+
+    fig.add_trace(go.Scatter(
+        x=x,
+        y=func(x, *popt),
+        mode="lines",
+        # name='Fit',
+        line=dict(color=method_color_mapping[method], width=3),
+        showlegend=False,
+        name=f"{popt[0]:.2f}N^{-popt[1]:.2f}",
+        hovertext=f"{popt[0]:.2f}N^{-popt[1]:.2f}",
+    ))
+
+st.plotly_chart(fig)

serve/tools/ptable.py

@@ -9,6 +9,13 @@ from bokeh.plotting import figure, show
 from bokeh.sampledata.periodic_table import elements
 from bokeh.transform import dodge, factor_cmap
 
+import streamlit as st
+from bokeh.plotting import figure
+from bokeh.models import ColumnDataSource, CustomJS, TapTool
+from bokeh.sampledata.periodic_table import elements
+from bokeh.transform import dodge, factor_cmap
+
+
 periods = ["I", "II", "III", "IV", "V", "VI", "VII"]
 groups = [str(x) for x in range(1, 19)]
 
@@ -40,12 +47,22 @@ TOOLTIPS = [
     ("Electronic configuration", "@{electronic configuration}"),
 ]
 
-p = figure(title="Periodic Table (omitting LA and AC Series)", width=1000, height=450,
-           x_range=groups, y_range=list(reversed(periods)),
-           tools="hover", toolbar_location=None, tooltips=TOOLTIPS)
+p = figure(title="Periodic Table (omitting LA and AC Series)", width=1000, height=450, 
+           x_range=groups, y_range=list(reversed(periods)), 
+           tools="hover,tap", toolbar_location=None, tooltips=TOOLTIPS)
+
+# Convert DataFrame to ColumnDataSource
+df["selected"] = False
+source = ColumnDataSource(df)
+
+r = p.rect("group", "period", 0.95, 0.95, source=source, fill_alpha=0.6, 
+           legend_field="metal", 
+           color=factor_cmap('metal', palette=list(cmap.values()), factors=list(cmap.keys())),
+           selection_color="firebrick", selection_alpha=0.9)
 
-r = p.rect("group", "period", 0.95, 0.95, source=df, fill_alpha=0.6, legend_field="metal",
-           color=factor_cmap('metal', palette=list(cmap.values()), factors=list(cmap.keys())))
+
+# r = p.rect("group", "period", 0.95, 0.95, source=df, fill_alpha=0.6, legend_field="metal",
+#            color=factor_cmap('metal', palette=list(cmap.values()), factors=list(cmap.keys())))
 
 text_props = dict(source=df, text_align="left", text_baseline="middle")
 
@@ -73,6 +90,69 @@ p.legend.orientation = "horizontal"
 p.legend.location ="top_center"
 p.hover.renderers = [r] # only hover element boxes
 
+print(source.dataspecs())
+
+# Create a CustomJS callback
+callback = CustomJS(args=dict(source=source), code="""
+        var data = source.data;
+        var selected_elements = [];
+        for (var i = 0; i < data.symbol.length; i++) {
+            if (data.selected[i]) { // Corrected if statement with braces
+                selected_elements.push(data.symbol[i]);
+            }
+        }
+        console.log('Selected elements:', selected_elements);
+        document.dispatchEvent(new CustomEvent("selection_event", {detail: JSON.stringify(selected_elements)}));
+    """)
+    # yield j
+    # st.rerun()
+    
+    
+
+# Add TapTool with the callback
+tap_tool = TapTool()
+p.add_tools(tap_tool)
+p.js_on_event('tap', callback)
+
 st.bokeh_chart(p, use_container_width=True)
 
-# show(p)
+# show(p)
+
+selected_info = st.empty()
+
+# Use session state to store selected elements
+if 'selected_elements' not in st.session_state:
+    st.session_state.selected_elements = []
+
+st.markdown("""
+<script>
+document.addEventListener('selection_event', function(e) {
+    var selected_elements = JSON.parse(e.detail);
+    window.parent.postMessage({
+        type: 'streamlit:set_session_state',
+        data: {
+            selected_elements: selected_elements
+        }
+    }, '*');
+});
+</script>
+""", unsafe_allow_html=True)
+
+# Display selected elements
+if st.session_state.selected_elements:
+    st.write("Selected Elements:")
+    for element in st.session_state.selected_elements:
+        st.write(f"{element['symbol']} ({element['name']}):")
+        st.write(f"  Atomic Number: {element['atomic_number']}")
+        st.write(f"  Atomic Mass: {element['atomic_mass']}")
+        st.write(f"  Type: {element['metal']}")
+        st.write("---")
+        
+else:
+    st.write("No elements selected. Click on elements in the periodic table to select them.")
+    # st.rerun()
+
+# Add a button to clear selection
+if st.button("Clear Selection"):
+    st.session_state.selected_elements = []
+    st.rerun()