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update combustion
Browse files- mlip_arena/models/externals.py +11 -11
- mlip_arena/models/registry.yaml +6 -0
- mlip_arena/tasks/combustion/alignn/hydrogen.json +1 -0
- mlip_arena/tasks/combustion/chgnet/hydrogen.json +1 -1
- mlip_arena/tasks/combustion/escn/hydrogen.json +1 -1
- mlip_arena/tasks/combustion/m3gnet/hydrogen.json +1 -0
- mlip_arena/tasks/combustion/mace-mp/hydrogen.json +0 -0
- mlip_arena/tasks/combustion/sevennet/hydrogen.json +1 -0
- serve/leaderboard.py +8 -12
mlip_arena/models/externals.py
CHANGED
@@ -5,7 +5,7 @@ from typing import Literal
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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
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@@ -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__(
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def calculate(self, atoms, properties=None, system_changes=None):
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super().calculate(atoms, properties, system_changes)
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@@ -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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)
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ckpt_cache = "/tmp/sevennet_checkpoint.pth.tar"
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response = requests.get(url
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with open(ckpt_cache, mode="wb") as file:
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device = device or get_freer_device()
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super().__init__(
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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, default_path
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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
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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__(path=dir_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)
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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)
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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__("7net-0", device=device, **kwargs)
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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
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@@ -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
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@@ -142,6 +146,8 @@ ALIGNN:
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prediction: EFS
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nvt: true
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npt: true
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SevenNet:
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module: externals
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- 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
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nvt: true
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npt: true
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- 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
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npt: true
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prediction: EFS
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nvt: true
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npt: true
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github: https://github.com/usnistgov/alignn
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doi: https://doi.org/10.1038/s41524-021-00650-1
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SevenNet:
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module: externals
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mlip_arena/tasks/combustion/alignn/hydrogen.json
ADDED
@@ -0,0 +1 @@
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[{"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}]
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mlip_arena/tasks/combustion/chgnet/hydrogen.json
CHANGED
@@ -1 +1 @@
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mlip_arena/tasks/combustion/escn/hydrogen.json
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[{"formula":"H256O128","reaction":"hydrogen","method":"eSCN(OC20)","natoms":384,"total_time_seconds":
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mlip_arena/tasks/combustion/m3gnet/hydrogen.json
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[{"formula":"H256O128","reaction":"hydrogen","method":"M3GNet","natoms":384,"total_time_seconds":39142.020383,"total_steps":55000,"target_steps":2000000,"steps_per_second":1.4051395268,"seconds_per_step":0.7116730979,"timesteps":[999,1999,2999,3999,4999,5999,6999,7999,8999,9999,10998,11998,12998,13998,14998,15998,16998,17998,18997,19997,20997,21997,22997,23996,24996,25996,26996,27996,28996,29996,30995,31995,32995,33995,34995,35995,36995,37995,38995,39994,40994,41994,42994,43994,44994,45993,46993,47993,48993,49993,50993,51993,52993,53993,54992,55992,56992,57992,58992,59992,60992,61992,62992],"nproducts":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"temperatures":[278.1618454477,283.696969209,303.6755464429,349.5712837869,322.6792603837,320.1010124872,312.7914613875,343.1849520658,327.7918844722,342.4570786966,317.6770535806,355.3462360308,334.8206442046,325.9879295542,345.4902922437,356.6987097928,391.1899374174,383.9048559001,350.6951495873,386.7836496123,382.6304424835,391.7035645393,410.0847022534,407.5474298038,390.6640401299,373.9615717633,418.8857020464,414.9326660162,394.4006858238,435.3612084608,426.4742325229,448.3010689935,444.4500224707,457.1614101777,474.5074430189,431.4959959882,407.8796622098,476.0885660253,473.770885575,451.3995183693,468.9214564249,455.5713467755,465.9930284562,446.994557228,501.2518002119,502.6081613348,501.7212826822,524.5671460643,504.2731318509,503.9600696525,486.7314483927,469.7956842017,504.6396365038,522.6636189139,509.1293033942,540.2430361317,546.673250177,522.759263918,552.3367774569,533.497120913,538.5284638521,538.3978274485,544.4310925162],"pressures":[0.034365536,0.0240053705,0.0285419368,0.043391098,0.036271515,0.0242560674,0.0245244065,0.0255612237,0.0587010644,0.0447296066,0.0455238,0.0534456919,0.031148673,0.0278920402,0.0336758569,0.0712907501,0.0617837335,0.0315673252,0.0680944324,0.0308707914,0.0401130505,0.0103924302,0.0487769557,0.0377368977,0.0102963299,0.0244089421,0.0470916629,0.0365938069,0.012004282,0.0407204914,0.0513731577,0.0547796264,0.0574332227,0.0366204213,0.0552931353,0.0368541901,0.0205456348,0.0424872227,0.0297305298,0.0156669095,0.0369472324,0.0449818124,0.0283406138,0.0007157391,0.0408773577,0.0570698865,0.0690986502,0.0636860815,0.0418454086,0.0254167744,0.0030708707,0.0472546443,0.0647528805,0.0699872424,0.0527485013,0.0628145051,0.0330205609,0.0555929281,0.0204227346,0.0314565462,-0.016260795,0.0811107531,0.040553463],"seconds_per_step_per_atom":0.0018533154}]
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mlip_arena/tasks/combustion/sevennet/hydrogen.json
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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 |
-
"
|
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 |
-
"
|
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=["
|
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$",
|
75 |
-
|
76 |
-
display_text="
|
77 |
),
|
78 |
"Paper": st.column_config.LinkColumn(
|
79 |
# validate="^https://[a-z]+\.streamlit\.app$",
|
80 |
-
|
81 |
-
display_text="
|
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 |
)
|