cyrusyc commited on
Commit
7c36d54
1 Parent(s): 152939f

update combustion

Browse files
mlip_arena/models/externals.py CHANGED
@@ -5,7 +5,7 @@ from typing import Literal
5
  import matgl
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  import requests
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  import torch
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- from alignn.ff.ff import AlignnAtomwiseCalculator, get_figshare_model_ff
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  from ase import Atoms
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  from chgnet.model.dynamics import CHGNetCalculator
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  from chgnet.model.model import CHGNet
@@ -219,7 +219,7 @@ class ALIGNN(AlignnAtomwiseCalculator):
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  def __init__(self, dir_path: str = "/tmp/alignn/", device=None, **kwargs) -> None:
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  model_path = get_figshare_model_ff(dir_path=dir_path)
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  device = device or get_freer_device()
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- super().__init__(model_path=model_path, device=device, **kwargs)
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  def calculate(self, atoms, properties=None, system_changes=None):
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  super().calculate(atoms, properties, system_changes)
@@ -227,15 +227,15 @@ class ALIGNN(AlignnAtomwiseCalculator):
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  class SevenNet(SevenNetCalculator):
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  def __init__(self, device=None, **kwargs):
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- url = (
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- "https://github.com/MDIL-SNU/SevenNet/raw/main/pretrained_potentials"
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- "/SevenNet_0__11July2024/checkpoint_sevennet_0.pth"
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- )
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- ckpt_cache = "/tmp/sevennet_checkpoint.pth.tar"
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- response = requests.get(url, timeout=20)
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- with open(ckpt_cache, mode="wb") as file:
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- file.write(response.content)
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  device = device or get_freer_device()
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- super().__init__(ckpt_cache, device=device, **kwargs)
 
5
  import matgl
6
  import requests
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  import torch
8
+ from alignn.ff.ff import AlignnAtomwiseCalculator, get_figshare_model_ff, default_path
9
  from ase import Atoms
10
  from chgnet.model.dynamics import CHGNetCalculator
11
  from chgnet.model.model import CHGNet
 
219
  def __init__(self, dir_path: str = "/tmp/alignn/", device=None, **kwargs) -> None:
220
  model_path = get_figshare_model_ff(dir_path=dir_path)
221
  device = device or get_freer_device()
222
+ super().__init__(path=dir_path, device=device, **kwargs)
223
 
224
  def calculate(self, atoms, properties=None, system_changes=None):
225
  super().calculate(atoms, properties, system_changes)
 
227
 
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  class SevenNet(SevenNetCalculator):
229
  def __init__(self, device=None, **kwargs):
230
+ # url = (
231
+ # "https://github.com/MDIL-SNU/SevenNet/raw/main/pretrained_potentials"
232
+ # "/SevenNet_0__11July2024/checkpoint_sevennet_0.pth"
233
+ # )
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+ # ckpt_cache = "/tmp/sevennet_checkpoint.pth.tar"
235
+ # response = requests.get(url)
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+ # with open(ckpt_cache, mode="wb") as file:
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+ # file.write(response.content)
238
 
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  device = device or get_freer_device()
240
 
241
+ super().__init__("7net-0", device=device, **kwargs)
mlip_arena/models/registry.yaml CHANGED
@@ -34,6 +34,8 @@ CHGNet:
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  - homonuclear-diatomics
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  - stability
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  - combustion
 
 
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  prediction: EFSM
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  nvt: true
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  npt: true
@@ -49,6 +51,8 @@ M3GNet:
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  - atomind/mptrj
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  gpu-tasks:
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  - homonuclear-diatomics
 
 
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  prediction: EFS
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  nvt: true
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  npt: true
@@ -142,6 +146,8 @@ ALIGNN:
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  prediction: EFS
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  nvt: true
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  npt: true
 
 
145
 
146
  SevenNet:
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  module: externals
 
34
  - homonuclear-diatomics
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  - stability
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  - combustion
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+ github: https://github.com/CederGroupHub/chgnet
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+ doi: https://doi.org/10.1038/s42256-023-00716-3
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  prediction: EFSM
40
  nvt: true
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  npt: true
 
51
  - atomind/mptrj
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  gpu-tasks:
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  - homonuclear-diatomics
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+ github: https://github.com/materialsvirtuallab/matgl
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+ doi: https://doi.org/10.1038/s43588-022-00349-3
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  prediction: EFS
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  nvt: true
58
  npt: true
 
146
  prediction: EFS
147
  nvt: true
148
  npt: true
149
+ github: https://github.com/usnistgov/alignn
150
+ doi: https://doi.org/10.1038/s41524-021-00650-1
151
 
152
  SevenNet:
153
  module: externals
mlip_arena/tasks/combustion/alignn/hydrogen.json ADDED
@@ -0,0 +1 @@
 
 
1
+ [{"formula":"H256O128","reaction":"hydrogen","method":"ALIGNN","natoms":384,"total_time_seconds":6164.61108,"total_steps":5000,"target_steps":2000000,"steps_per_second":0.8110811753,"seconds_per_step":1.232922216,"timesteps":[999,1999,2999,3999,4999,5999,6998],"nproducts":[3,1,3,1,3,1,1],"temperatures":[313.0468146165,301.8534691837,310.4575158955,313.9334209657,320.7933195045,323.9382054092,331.8809755899],"pressures":[],"seconds_per_step_per_atom":0.0032107349}]
mlip_arena/tasks/combustion/chgnet/hydrogen.json CHANGED
@@ -1 +1 @@
1
- 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mlip_arena/tasks/combustion/escn/hydrogen.json CHANGED
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mlip_arena/tasks/combustion/m3gnet/hydrogen.json ADDED
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mlip_arena/tasks/combustion/mace-mp/hydrogen.json CHANGED
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mlip_arena/tasks/combustion/sevennet/hydrogen.json ADDED
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serve/leaderboard.py CHANGED
@@ -3,11 +3,9 @@ from pathlib import Path
3
  import pandas as pd
4
  import streamlit as st
5
 
6
- # from mlip_arena.models.utils import MLIPEnum, REGISTRY
7
  from mlip_arena.models import REGISTRY
8
 
9
  DATA_DIR = Path("mlip_arena/tasks/diatomics")
10
- # methods = ["MACE-MP", "Equiformer", "CHGNet", "MACE-OFF", "eSCN", "ALIGNN"]
11
 
12
  dfs = [pd.read_json(DATA_DIR / REGISTRY[model].get("family") / "homonuclear-diatomics.json") for model in REGISTRY]
13
  df = pd.concat(dfs, ignore_index=True)
@@ -15,7 +13,7 @@ df = pd.concat(dfs, ignore_index=True)
15
 
16
  table = pd.DataFrame(columns=[
17
  "Model",
18
- "Supported elements",
19
  # "No. of reversed forces",
20
  # "Energy-consistent forces",
21
  "Prediction",
@@ -31,7 +29,7 @@ for model in REGISTRY:
31
  metadata = REGISTRY.get(model, {})
32
  new_row = {
33
  "Model": model,
34
- "Supported elements": len(rows["name"].unique()),
35
  # "No. of reversed forces": None, # Replace with actual logic if available
36
  # "Energy-consistent forces": None, # Replace with actual logic if available
37
  "Prediction": metadata.get("prediction", None),
@@ -47,7 +45,7 @@ table.set_index("Model", inplace=True)
47
 
48
  s = table.style.background_gradient(
49
  cmap="PuRd",
50
- subset=["Supported elements"],
51
  vmin=0, vmax=120
52
  )
53
 
@@ -62,23 +60,21 @@ MLIP Arena is a platform for benchmarking foundation machine learning interatomi
62
  The benchmarks are designed to evaluate the readiness and reliability of open-source, open-weight models to reproduce the qualitatively or quantitatively correct physics.
63
  """, unsafe_allow_html=True)
64
 
65
- st.header("Summary", divider=True)
66
 
67
  st.dataframe(
68
  s,
69
  use_container_width=True,
70
  column_config={
71
  "Code": st.column_config.LinkColumn(
72
- # "GitHub",
73
- # help="The top trending Streamlit apps",
74
  # validate="^https://[a-z]+\.streamlit\.app$",
75
- max_chars=100,
76
- display_text="GitHub",
77
  ),
78
  "Paper": st.column_config.LinkColumn(
79
  # validate="^https://[a-z]+\.streamlit\.app$",
80
- max_chars=100,
81
- display_text="arXiv",
82
  ),
83
  },
84
  )
 
3
  import pandas as pd
4
  import streamlit as st
5
 
 
6
  from mlip_arena.models import REGISTRY
7
 
8
  DATA_DIR = Path("mlip_arena/tasks/diatomics")
 
9
 
10
  dfs = [pd.read_json(DATA_DIR / REGISTRY[model].get("family") / "homonuclear-diatomics.json") for model in REGISTRY]
11
  df = pd.concat(dfs, ignore_index=True)
 
13
 
14
  table = pd.DataFrame(columns=[
15
  "Model",
16
+ "Element Coverage",
17
  # "No. of reversed forces",
18
  # "Energy-consistent forces",
19
  "Prediction",
 
29
  metadata = REGISTRY.get(model, {})
30
  new_row = {
31
  "Model": model,
32
+ "Element Coverage": len(rows["name"].unique()),
33
  # "No. of reversed forces": None, # Replace with actual logic if available
34
  # "Energy-consistent forces": None, # Replace with actual logic if available
35
  "Prediction": metadata.get("prediction", None),
 
45
 
46
  s = table.style.background_gradient(
47
  cmap="PuRd",
48
+ subset=["Element Coverage"],
49
  vmin=0, vmax=120
50
  )
51
 
 
60
  The benchmarks are designed to evaluate the readiness and reliability of open-source, open-weight models to reproduce the qualitatively or quantitatively correct physics.
61
  """, unsafe_allow_html=True)
62
 
63
+ # st.header("Summary", divider=True)
64
 
65
  st.dataframe(
66
  s,
67
  use_container_width=True,
68
  column_config={
69
  "Code": st.column_config.LinkColumn(
 
 
70
  # validate="^https://[a-z]+\.streamlit\.app$",
71
+ width="medium",
72
+ display_text="Link",
73
  ),
74
  "Paper": st.column_config.LinkColumn(
75
  # validate="^https://[a-z]+\.streamlit\.app$",
76
+ width="medium",
77
+ display_text="Link",
78
  ),
79
  },
80
  )