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1 Parent(s): 7f5709e

add combustion MD runtime speed

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Files changed (7) hide show
  1. mlip_arena/models/registry.yaml +1 -1
  2. mlip_arena/tasks/combustion/alignn/hydrogen.json +1 -1
  3. mlip_arena/tasks/combustion/chgnet/hydrogen.json +1 -1
  4. mlip_arena/tasks/combustion/escn/hydrogen.json +1 -1
  5. mlip_arena/tasks/combustion/m3gnet/hydrogen.json +1 -1
  6. mlip_arena/tasks/combustion/sevennet/hydrogen.json +1 -1
  7. serve/tasks/combustion.py +26 -8
mlip_arena/models/registry.yaml CHANGED
@@ -174,7 +174,7 @@ SevenNet:
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  - combustion
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  github: https://github.com/MDIL-SNU/SevenNet
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  doi: https://doi.org/10.1021/acs.jctc.4c00190
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- date: 2024-02-06
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  prediction: EFS
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  nvt: true
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  npt: true
 
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  - combustion
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  github: https://github.com/MDIL-SNU/SevenNet
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  doi: https://doi.org/10.1021/acs.jctc.4c00190
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+ date: 2024-07-11
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  prediction: EFS
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  nvt: true
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  npt: true
mlip_arena/tasks/combustion/alignn/hydrogen.json CHANGED
@@ -1 +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":[999,1999,2999,3999,4999,5999,6998,7997],"nproducts":[3,1,3,1,3,1,1,5],"temperatures":[313.0468146165,301.8534691837,310.4575158955,313.9334209657,320.7933195045,323.9382054092,331.8809755899,322.2083052805],"pressures":[],"seconds_per_step_per_atom":0.0032107349}]
 
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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":[999,1999,2999,3999,4999,5999,6998,7997,8996],"nproducts":[3,1,3,1,3,1,1,5,1],"temperatures":[313.0468146165,301.8534691837,310.4575158955,313.9334209657,320.7933195045,323.9382054092,331.8809755899,322.2083052805,342.1133398233],"pressures":[],"seconds_per_step_per_atom":0.0032107349}]
mlip_arena/tasks/combustion/chgnet/hydrogen.json CHANGED
@@ -1 +1 @@
1
- [{"formula":"H256O128","reaction":"hydrogen","method":"CHGNet","natoms":384,"total_time_seconds":51529.690812,"total_steps":318000,"target_steps":2000000,"steps_per_second":6.1711994578,"seconds_per_step":0.1620430529,"timesteps":[999,1999,2999,3999,4999,5999,6999,7999,8999,9999,10999,11999,12999,13999,14999,15999,16999,17999,18999,19999,20999,21999,22999,23999,24999,25999,26999,27999,28999,29999,30999,31999,32999,33999,34999,35999,36999,37999,38999,39999,40998,41998,42998,43998,44998,45998,46998,47998,48998,49998,50998,51998,52998,53998,54998,55998,56998,57998,58998,59998,60998,61998,62998,63998,64998,65998,66998,67998,68998,69998,70998,71998,72998,73998,74998,75998,76998,77998,78998,79998,80998,81998,82997,83997,84997,85997,86997,87997,88997,89997,90997,91997,92997,93997,94997,95997,96997,97997,98997,99997,100997,101997,102997,103997,104997,105997,106997,107997,108997,109997,110997,111997,112997,113997,114997,115997,116997,117997,118997,119997,120997,121997,122996,123996,124996,125996,126996,127996,128996,129996,130996,131996,132996,133996,134996,135996,136996,137996,138996,139996,140996,141996,142996,143996,144996,145996,146996,147996,148996,149996,150996,151996,152996,153996,154996,155996,156996,157996,158996,159996,160995,161995,162995,163995,164995,165995,166995,167995,168995,169995,170995,171995,172995,173995,174995,175995,176995,177995,178995,179995,180995,181995,182995,183995,184995,185995,186995,187995,188995,189995,190995,191995,192995,193995,194995,195995,196995,197995,198995,199995,200995,201995,202995,203995,204995,205995,206995,207994,208994,209994,210994,211994,212994,213994,214994,215994,216994,217994,218994,219994,220994,221994,222994,223994,224994,225994,226994,227994,228994,229994,230994,231994,232994,233994,234994,235994,236994,237994,238993,239993,240993,241993,242993,243993,244993,245993,246993,247993,248993,249993,250993,251993,252993,253993,254993,255993,256993,257993,258993,259993,260993,261993,262993,263993,264993,265993,266993,267992,268992,269992,270992,271992,272992,273992,274992,275992,276992,277992,278992,279992,280992,281992,282992,283992,284992,285992,286992,287992,288992,289992,290992,291992,292992,293992,294992,295992,296992,297992,298991,299991,300991,301991,302991,303991,304991,305991,306991,307991,308991,309991,310991,311991,312991,313991,314991,315991,316991,317991,318991,319991,320991,321991,322991,323991,324991,325991,326991],"nproducts":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,2,2,2,2,2,2,2,2,2,2,3,2,2,3,2,2,3,4,4,4,4,4,4,5,6,6,6,6,7,7,7,6,6,6,6,6,6,6,6,6,7,8,8,9,8,8,8,9,8,7,8,8,8,9,10,10,13,13,13,13,12,12,12,12,12,12,12,11,12,12,13,12,13,12,13,12,12,12,12,12,13,13,12,12,12,13,12,12,12,14,14,15,15,16,16,16,19,20,19,20,21,23,24,24,26,27,29,30,30,31,32,32,32,34,34,34,35,36,36,36,36,36,37,36,36,39,40,39,40,40,40,41,40,41,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,39,40,40,39,40,40,40,40,41,40,39,40,40,40,39,40,40,40,40,40,40,41,41,40,40,40,40,40,41,40,40,40,40,40,39,40,40,40,40,40,40,41,41,43,42,44,44,45,44,44,44,44,44,43,44,44,45,45,46,45,46],"temperatures":[320.894640339,330.0580312749,312.5184535283,297.0096591849,318.9622748059,294.1133651832,335.0567601048,340.9913931381,361.108055322,341.5113227087,349.5400393164,338.9894851104,351.8091886277,359.8059191316,379.1760451212,377.680486901,367.5833436879,364.1090606034,394.334886932,407.5577715376,396.6642996576,411.4238020251,369.0251205084,364.9241355357,375.72407981,417.7418598106,378.4918277733,415.9247297861,444.014440563,430.8706433954,446.5447209093,426.7247261869,403.1678884548,463.5421718967,440.2203521289,442.3330092133,453.935618185,472.3935210233,443.6099583752,481.0236780405,462.9973881175,450.3288394279,483.3954880795,440.971878962,472.3267049842,450.2453763259,453.8268545117,498.311769143,474.0552152173,497.7715310154,484.696853851,488.1329184808,506.7599888887,491.0137978562,524.5882373504,499.8942247977,515.3217621762,571.82914913,610.9708692226,581.0187648229,552.3090172425,503.964710724,529.9753144416,565.7179047223,526.1674768943,575.8404932445,547.6322633634,519.6690174456,564.9716929283,612.8052822757,579.2214042808,620.250138166,623.9902505709,576.4615350104,592.3713069756,643.6480021391,616.0249032405,676.5173318656,688.9910869222,644.244372246,612.7276797684,593.2913309968,605.6295587862,671.1749610647,656.7541998741,629.9979425869,710.5462742461,664.3476103415,643.329780187,685.8468813753,665.2000627526,666.9913860774,701.0194422688,690.0386398861,637.8403096223,657.2087945322,675.507754438,706.1931028319,751.6500345533,688.2180694081,769.0765272884,732.1946134232,747.5172792549,687.0809644983,622.8782389666,734.0018112518,706.8457427813,751.5146953222,748.6327233429,680.6304107712,707.557341266,784.9878717091,744.1247876641,771.8088020561,741.41639195,742.6138104578,782.7649185595,722.8085135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mlip_arena/tasks/combustion/escn/hydrogen.json CHANGED
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mlip_arena/tasks/combustion/m3gnet/hydrogen.json CHANGED
@@ -1 +1 @@
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3756028,0.0021241802,0.0031381173,0.0019277796,0.00179904,0.0013761574,0.0016587155,0.0022954313,0.0010364417,0.0005820463,0.0012597044,0.0026144085,0.0020752917,0.0018099139,0.002299871,0.0022238498,0.0030126652,0.0014267699,0.0009217545,0.0015232567,0.0017208116,0.0014360472,0.0013378213,0.00276605,0.0011321215,0.0018773209,0.0019401644,0.0015404972,0.0014040798,0.0019332441,0.0011860713,0.0021070119,0.0014924106,0.0033087927,0.0024321289,0.0003866705,0.0026047453,0.0015169874,0.0018952305,0.0018312265,0.0030421308,0.0013595679,0.0020522536,0.0015542648,0.0023713047,0.0016388527,0.0029397185,0.0022834865,0.0019490078,0.002116816,0.0010029237,0.0020072067,0.0018019748,0.0021525366,0.0014504485,0.0015233049,0.0015369044,0.001001657,0.0023063953,0.0018742887,0.0016700183],"seconds_per_step_per_atom":0.0001096672}]
serve/tasks/combustion.py CHANGED
@@ -1,12 +1,9 @@
1
  from pathlib import Path
2
 
3
- import numpy as np
4
  import pandas as pd
5
  import plotly.colors as pcolors
6
- import plotly.express as px
7
  import plotly.graph_objects as go
8
  import streamlit as st
9
- from scipy.optimize import curve_fit
10
 
11
  from mlip_arena.models import REGISTRY
12
 
@@ -21,7 +18,7 @@ st.markdown("### Methods")
21
  container = st.container(border=True)
22
  valid_models = [model for model, metadata in REGISTRY.items() if Path(__file__).stem in metadata.get("gpu-tasks", [])]
23
 
24
- models = container.multiselect("MLIPs", valid_models, ["MACE-MP(M)", "CHGNet", "EquiformerV2(OC22)", "SevenNet"])
25
 
26
  st.markdown("### Settings")
27
  vis = st.container(border=True)
@@ -69,7 +66,7 @@ for method in df["method"].unique():
69
  go.Scatter(
70
  x=row["timesteps"],
71
  y=row["nproducts"],
72
- mode='lines',
73
  name=method,
74
  line=dict(color=method_color_mapping[method]),
75
  showlegend=True,
@@ -84,7 +81,7 @@ fig.update_layout(
84
 
85
  st.plotly_chart(fig)
86
 
87
- # tempearture
88
 
89
  fig = go.Figure()
90
 
@@ -94,7 +91,7 @@ for method in df["method"].unique():
94
  go.Scatter(
95
  x=row["timesteps"],
96
  y=row["temperatures"],
97
- mode='markers',
98
  name=method,
99
  line=dict(color=method_color_mapping[method]),
100
  showlegend=True,
@@ -106,7 +103,7 @@ fig.add_trace(
106
  go.Line(
107
  x=[0, target_steps/3, target_steps/3*2, target_steps],
108
  y=[300, 3000, 3000, 300],
109
- mode='lines',
110
  name="Target",
111
  line=dict(
112
  dash="dash",
@@ -130,3 +127,24 @@ fig.update_layout(
130
  )
131
 
132
  st.plotly_chart(fig)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  from pathlib import Path
2
 
 
3
  import pandas as pd
4
  import plotly.colors as pcolors
 
5
  import plotly.graph_objects as go
6
  import streamlit as st
 
7
 
8
  from mlip_arena.models import REGISTRY
9
 
 
18
  container = st.container(border=True)
19
  valid_models = [model for model, metadata in REGISTRY.items() if Path(__file__).stem in metadata.get("gpu-tasks", [])]
20
 
21
+ models = container.multiselect("MLIPs", valid_models, ["MACE-MP(M)", "CHGNet", "M3GNet", "EquiformerV2(OC22)", "SevenNet"])
22
 
23
  st.markdown("### Settings")
24
  vis = st.container(border=True)
 
66
  go.Scatter(
67
  x=row["timesteps"],
68
  y=row["nproducts"],
69
+ mode="lines",
70
  name=method,
71
  line=dict(color=method_color_mapping[method]),
72
  showlegend=True,
 
81
 
82
  st.plotly_chart(fig)
83
 
84
+ # tempearture
85
 
86
  fig = go.Figure()
87
 
 
91
  go.Scatter(
92
  x=row["timesteps"],
93
  y=row["temperatures"],
94
+ mode="markers",
95
  name=method,
96
  line=dict(color=method_color_mapping[method]),
97
  showlegend=True,
 
103
  go.Line(
104
  x=[0, target_steps/3, target_steps/3*2, target_steps],
105
  y=[300, 3000, 3000, 300],
106
+ mode="lines",
107
  name="Target",
108
  line=dict(
109
  dash="dash",
 
127
  )
128
 
129
  st.plotly_chart(fig)
130
+
131
+ # MD runtime speed
132
+
133
+ fig = go.Figure()
134
+
135
+ fig.add_trace(
136
+ go.Bar(
137
+ x=df["method"],
138
+ y=df["steps_per_second"],
139
+ marker_color="blue",
140
+ opacity=0.75,
141
+ )
142
+ )
143
+
144
+ fig.update_layout(
145
+ title="MD runtime speed (on single A100 GPU)",
146
+ xaxis_title="Method",
147
+ yaxis_title="Steps per second",
148
+ )
149
+
150
+ st.plotly_chart(fig)