Spaces:
Running
Running
Yuan (Cyrus) Chiang
commited on
Commit
•
d937c80
1
Parent(s):
f9d3b3b
Add COM drift for combustion (#10)
Browse files* add chgnet checkpoint info
* add com drifts + animation; add streamlit test
* remove test interdependency
- mlip_arena/models/registry.yaml +1 -1
- mlip_arena/tasks/combustion/chgnet/hydrogen.json +1 -1
- mlip_arena/tasks/combustion/equiformer/hydrogen.json +1 -1
- mlip_arena/tasks/combustion/escn/hydrogen.json +0 -0
- mlip_arena/tasks/combustion/m3gnet/hydrogen.json +1 -1
- mlip_arena/tasks/combustion/mace-mp/hydrogen.json +0 -0
- mlip_arena/tasks/combustion/orb/hydrogen.json +0 -0
- mlip_arena/tasks/combustion/sevennet/hydrogen.json +0 -0
- mlip_arena/tasks/combustion/water.ipynb +55 -64
- mlip_arena/tasks/diatomics/run.ipynb +0 -0
- mlip_arena/tasks/registry.yaml +1 -1
- pyproject.toml +1 -1
- requirements.txt +1 -1
- serve/leaderboard.py +8 -6
- serve/ranks/homonuclear-diatomics.py +1 -6
- serve/tasks/combustion.py +180 -23
- serve/tasks/homonuclear-diatomics.py +139 -115
mlip_arena/models/registry.yaml
CHANGED
@@ -28,7 +28,7 @@ CHGNet:
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class: CHGNet
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family: chgnet
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package: chgnet==0.3.8
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-
checkpoint:
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username: cyrusyc
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last-update: 2024-07-08T00:00:00
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datetime: 2024-07-08T00:00:00
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class: CHGNet
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family: chgnet
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package: chgnet==0.3.8
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+
checkpoint: v0.3.0
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username: cyrusyc
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last-update: 2024-07-08T00:00:00
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datetime: 2024-07-08T00:00:00
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mlip_arena/tasks/combustion/chgnet/hydrogen.json
CHANGED
@@ -1 +1 @@
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1 |
-
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mlip_arena/tasks/combustion/equiformer/hydrogen.json
CHANGED
@@ -1 +1 @@
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mlip_arena/tasks/combustion/mace-mp/hydrogen.json
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mlip_arena/tasks/combustion/orb/hydrogen.json
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mlip_arena/tasks/combustion/sevennet/hydrogen.json
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mlip_arena/tasks/combustion/water.ipynb
CHANGED
@@ -2,7 +2,7 @@
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"cells": [
|
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{
|
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"cell_type": "code",
|
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-
"execution_count":
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"metadata": {},
|
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"outputs": [],
|
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"source": [
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@@ -99,7 +99,7 @@
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},
|
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{
|
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"cell_type": "code",
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-
"execution_count":
|
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"metadata": {
|
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"tags": []
|
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},
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@@ -119,7 +119,7 @@
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|
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},
|
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{
|
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"cell_type": "code",
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-
"execution_count":
|
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"metadata": {},
|
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"outputs": [
|
125 |
{
|
@@ -130,7 +130,7 @@
|
|
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"\n",
|
131 |
"#SBATCH -A matgen\n",
|
132 |
"#SBATCH --mem=0\n",
|
133 |
-
"#SBATCH -t
|
134 |
"#SBATCH -J combustion-water\n",
|
135 |
"#SBATCH -q regular\n",
|
136 |
"#SBATCH -N 1\n",
|
@@ -140,18 +140,9 @@
|
|
140 |
"source ~/.bashrc\n",
|
141 |
"module load python\n",
|
142 |
"source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\n",
|
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:17.377 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Created task run 'md-4' for task 'md'\n",
|
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"</pre>\n"
|
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],
|
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"text/plain": [
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+
"17:24:17.377 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Created task run 'md-4' for task 'md'\n"
|
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]
|
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},
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"metadata": {},
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{
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:17.407 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Created task run 'md-1' for task 'md'\n",
|
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"</pre>\n"
|
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],
|
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"text/plain": [
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+
"17:24:17.407 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Created task run 'md-1' for task 'md'\n"
|
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]
|
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},
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"metadata": {},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:17.410 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Created task run 'md-3' for task 'md'\n",
|
312 |
"</pre>\n"
|
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],
|
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"text/plain": [
|
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+
"17:24:17.410 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Created task run 'md-3' for task 'md'\n"
|
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]
|
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},
|
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"metadata": {},
|
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{
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"data": {
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"text/html": [
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:17.413 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Created task run 'md-5' for task 'md'\n",
|
325 |
"</pre>\n"
|
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],
|
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"text/plain": [
|
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+
"17:24:17.413 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Created task run 'md-5' for task 'md'\n"
|
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]
|
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},
|
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"metadata": {},
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{
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"data": {
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"text/html": [
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:17.417 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Created task run 'md-7' for task 'md'\n",
|
338 |
"</pre>\n"
|
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],
|
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"text/plain": [
|
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+
"17:24:17.417 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Created task run 'md-7' for task 'md'\n"
|
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]
|
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},
|
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"metadata": {},
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{
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"data": {
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"text/html": [
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:17.420 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Created task run 'md-8' for task 'md'\n",
|
351 |
"</pre>\n"
|
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],
|
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"text/plain": [
|
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+
"17:24:17.420 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Created task run 'md-8' for task 'md'\n"
|
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]
|
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},
|
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"metadata": {},
|
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{
|
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"data": {
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"text/html": [
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:17.423 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Created task run 'md-0' for task 'md'\n",
|
364 |
"</pre>\n"
|
365 |
],
|
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"text/plain": [
|
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+
"17:24:17.423 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Created task run 'md-0' for task 'md'\n"
|
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]
|
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},
|
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"metadata": {},
|
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{
|
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"data": {
|
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"text/html": [
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:17.425 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Created task run 'md-2' for task 'md'\n",
|
377 |
"</pre>\n"
|
378 |
],
|
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"text/plain": [
|
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+
"17:24:17.425 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Created task run 'md-2' for task 'md'\n"
|
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]
|
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},
|
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"metadata": {},
|
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{
|
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"data": {
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"text/html": [
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:17.427 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Created task run 'md-6' for task 'md'\n",
|
390 |
"</pre>\n"
|
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],
|
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"text/plain": [
|
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+
"17:24:17.427 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Created task run 'md-6' for task 'md'\n"
|
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]
|
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},
|
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"metadata": {},
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{
|
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"data": {
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"text/html": [
|
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:17.545 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Created task run 'md-9' for task 'md'\n",
|
403 |
"</pre>\n"
|
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],
|
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"text/plain": [
|
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+
"17:24:17.545 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Created task run 'md-9' for task 'md'\n"
|
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]
|
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},
|
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"metadata": {},
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{
|
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"data": {
|
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"text/html": [
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:17.948 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Submitted task run 'md-7' for execution.\n",
|
416 |
"</pre>\n"
|
417 |
],
|
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"text/plain": [
|
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+
"17:24:17.948 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Submitted task run 'md-7' for execution.\n"
|
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]
|
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},
|
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"metadata": {},
|
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{
|
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"data": {
|
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"text/html": [
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:17.958 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Submitted task run 'md-3' for execution.\n",
|
429 |
"</pre>\n"
|
430 |
],
|
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"text/plain": [
|
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+
"17:24:17.958 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Submitted task run 'md-3' for execution.\n"
|
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]
|
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},
|
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"metadata": {},
|
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{
|
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"data": {
|
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"text/html": [
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:18.055 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Submitted task run 'md-4' for execution.\n",
|
442 |
"</pre>\n"
|
443 |
],
|
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"text/plain": [
|
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+
"17:24:18.055 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Submitted task run 'md-4' for execution.\n"
|
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]
|
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},
|
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"metadata": {},
|
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{
|
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"data": {
|
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"text/html": [
|
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:18.062 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Submitted task run 'md-1' for execution.\n",
|
455 |
"</pre>\n"
|
456 |
],
|
457 |
"text/plain": [
|
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+
"17:24:18.062 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Submitted task run 'md-1' for execution.\n"
|
459 |
]
|
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},
|
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"metadata": {},
|
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{
|
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"data": {
|
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"text/html": [
|
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:18.097 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Submitted task run 'md-0' for execution.\n",
|
468 |
"</pre>\n"
|
469 |
],
|
470 |
"text/plain": [
|
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+
"17:24:18.097 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Submitted task run 'md-0' for execution.\n"
|
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]
|
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},
|
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"metadata": {},
|
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|
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{
|
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"data": {
|
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"text/html": [
|
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:18.108 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Submitted task run 'md-9' for execution.\n",
|
481 |
"</pre>\n"
|
482 |
],
|
483 |
"text/plain": [
|
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+
"17:24:18.108 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Submitted task run 'md-9' for execution.\n"
|
485 |
]
|
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},
|
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"metadata": {},
|
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|
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{
|
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"data": {
|
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"text/html": [
|
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:18.114 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Submitted task run 'md-6' for execution.\n",
|
494 |
"</pre>\n"
|
495 |
],
|
496 |
"text/plain": [
|
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+
"17:24:18.114 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Submitted task run 'md-6' for execution.\n"
|
498 |
]
|
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},
|
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"metadata": {},
|
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|
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{
|
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"data": {
|
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"text/html": [
|
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:18.147 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Submitted task run 'md-5' for execution.\n",
|
507 |
"</pre>\n"
|
508 |
],
|
509 |
"text/plain": [
|
510 |
+
"17:24:18.147 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Submitted task run 'md-5' for execution.\n"
|
511 |
]
|
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},
|
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"metadata": {},
|
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{
|
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"data": {
|
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"text/html": [
|
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:18.259 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Submitted task run 'md-8' for execution.\n",
|
520 |
"</pre>\n"
|
521 |
],
|
522 |
"text/plain": [
|
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+
"17:24:18.259 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Submitted task run 'md-8' for execution.\n"
|
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]
|
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},
|
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"metadata": {},
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{
|
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"data": {
|
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"text/html": [
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">17:24:18.281 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'prompt-newt'</span> - Submitted task run 'md-2' for execution.\n",
|
533 |
"</pre>\n"
|
534 |
],
|
535 |
"text/plain": [
|
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+
"17:24:18.281 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Submitted task run 'md-2' for execution.\n"
|
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]
|
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},
|
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"metadata": {},
|
mlip_arena/tasks/diatomics/run.ipynb
CHANGED
The diff for this file is too large to render.
See raw diff
|
|
mlip_arena/tasks/registry.yaml
CHANGED
@@ -13,7 +13,7 @@ Combustion:
|
|
13 |
category: Molecular dynamics
|
14 |
task-page: combustion
|
15 |
task-layout: centered
|
16 |
-
rank-page:
|
17 |
# nacl:
|
18 |
# last-update: 2024-03-25T14:30:00 # TODO: Fake datetime
|
19 |
# alexandria:
|
|
|
13 |
category: Molecular dynamics
|
14 |
task-page: combustion
|
15 |
task-layout: centered
|
16 |
+
rank-page: combustion
|
17 |
# nacl:
|
18 |
# last-update: 2024-03-25T14:30:00 # TODO: Fake datetime
|
19 |
# alexandria:
|
pyproject.toml
CHANGED
@@ -50,7 +50,7 @@ run = [
|
|
50 |
"alignn==2024.5.27"
|
51 |
]
|
52 |
app = [
|
53 |
-
"streamlit",
|
54 |
"plotly",
|
55 |
"bokeh==2.4.3",
|
56 |
"statsmodels"
|
|
|
50 |
"alignn==2024.5.27"
|
51 |
]
|
52 |
app = [
|
53 |
+
"streamlit==1.38.0",
|
54 |
"plotly",
|
55 |
"bokeh==2.4.3",
|
56 |
"statsmodels"
|
requirements.txt
CHANGED
@@ -1,4 +1,4 @@
|
|
1 |
-
streamlit
|
2 |
plotly
|
3 |
numpy
|
4 |
scipy
|
|
|
1 |
+
streamlit==1.38.0
|
2 |
plotly
|
3 |
numpy
|
4 |
scipy
|
serve/leaderboard.py
CHANGED
@@ -98,16 +98,18 @@ for task in TASKS:
|
|
98 |
continue
|
99 |
|
100 |
st.header(task, divider=True)
|
101 |
-
st.page_link(
|
102 |
-
f"tasks/{TASKS[task]['task-page']}.py",
|
103 |
-
label="Link to task page",
|
104 |
-
icon=":material/link:",
|
105 |
-
)
|
106 |
|
107 |
task_module = importlib.import_module(f"ranks.{TASKS[task]['rank-page']}")
|
108 |
|
109 |
# Call the function from the imported module
|
110 |
if hasattr(task_module, "render"):
|
111 |
task_module.render()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
112 |
else:
|
113 |
-
st.write("Results for the task are not available yet.")
|
|
|
98 |
continue
|
99 |
|
100 |
st.header(task, divider=True)
|
|
|
|
|
|
|
|
|
|
|
101 |
|
102 |
task_module = importlib.import_module(f"ranks.{TASKS[task]['rank-page']}")
|
103 |
|
104 |
# Call the function from the imported module
|
105 |
if hasattr(task_module, "render"):
|
106 |
task_module.render()
|
107 |
+
st.page_link(
|
108 |
+
f"tasks/{TASKS[task]['task-page']}.py",
|
109 |
+
label="Task page",
|
110 |
+
icon=":material/link:",
|
111 |
+
)
|
112 |
+
# if st.button(f"Go to task page"):
|
113 |
+
# st.switch_page(f"tasks/{TASKS[task]['task-page']}.py")
|
114 |
else:
|
115 |
+
st.write("Results for the task are not available yet.")
|
serve/ranks/homonuclear-diatomics.py
CHANGED
@@ -2,12 +2,7 @@ from pathlib import Path
|
|
2 |
|
3 |
import numpy as np
|
4 |
import pandas as pd
|
5 |
-
import plotly.colors as pcolors
|
6 |
-
import plotly.graph_objects as go
|
7 |
import streamlit as st
|
8 |
-
from ase.data import chemical_symbols
|
9 |
-
from plotly.subplots import make_subplots
|
10 |
-
from scipy.interpolate import CubicSpline
|
11 |
|
12 |
from mlip_arena.models import REGISTRY as MODELS
|
13 |
|
@@ -131,4 +126,4 @@ def render():
|
|
131 |
s,
|
132 |
use_container_width=True,
|
133 |
)
|
134 |
-
# return table
|
|
|
2 |
|
3 |
import numpy as np
|
4 |
import pandas as pd
|
|
|
|
|
5 |
import streamlit as st
|
|
|
|
|
|
|
6 |
|
7 |
from mlip_arena.models import REGISTRY as MODELS
|
8 |
|
|
|
126 |
s,
|
127 |
use_container_width=True,
|
128 |
)
|
129 |
+
# return table
|
serve/tasks/combustion.py
CHANGED
@@ -3,10 +3,11 @@ from pathlib import Path
|
|
3 |
import numpy as np
|
4 |
import pandas as pd
|
5 |
import plotly.colors as pcolors
|
|
|
6 |
import plotly.graph_objects as go
|
7 |
import streamlit as st
|
8 |
|
9 |
-
from mlip_arena.models import REGISTRY
|
10 |
|
11 |
DATA_DIR = Path("mlip_arena/tasks/combustion")
|
12 |
|
@@ -17,12 +18,24 @@ st.markdown("""
|
|
17 |
|
18 |
st.markdown("### Methods")
|
19 |
container = st.container(border=True)
|
20 |
-
valid_models = [
|
|
|
|
|
|
|
|
|
21 |
|
22 |
models = container.multiselect(
|
23 |
-
"MLIPs",
|
24 |
-
valid_models,
|
25 |
-
[
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
26 |
)
|
27 |
|
28 |
st.markdown("### Settings")
|
@@ -38,7 +51,6 @@ color_palettes.pop("__all__", None)
|
|
38 |
|
39 |
palette_names = list(color_palettes.keys())
|
40 |
palette_colors = list(color_palettes.values())
|
41 |
-
|
42 |
palette_name = vis.selectbox("Color sequence", options=palette_names, index=22)
|
43 |
|
44 |
color_sequence = color_palettes[palette_name]
|
@@ -46,14 +58,25 @@ color_sequence = color_palettes[palette_name]
|
|
46 |
if not models:
|
47 |
st.stop()
|
48 |
|
49 |
-
families = [REGISTRY[str(model)]["family"] for model in models]
|
50 |
|
51 |
-
|
52 |
-
|
53 |
-
|
54 |
-
|
55 |
-
|
56 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
57 |
|
58 |
method_color_mapping = {
|
59 |
method: color_sequence[i % len(color_sequence)]
|
@@ -98,7 +121,11 @@ for method in df["method"].unique():
|
|
98 |
y=row["temperatures"],
|
99 |
mode="markers",
|
100 |
name=method,
|
101 |
-
line=dict(
|
|
|
|
|
|
|
|
|
102 |
showlegend=True,
|
103 |
),
|
104 |
)
|
@@ -106,13 +133,11 @@ for method in df["method"].unique():
|
|
106 |
target_steps = df["target_steps"].iloc[0]
|
107 |
fig.add_trace(
|
108 |
go.Line(
|
109 |
-
x=[0, target_steps/3, target_steps/3*2, target_steps],
|
110 |
y=[300, 3000, 3000, 300],
|
111 |
mode="lines",
|
112 |
name="Target",
|
113 |
-
line=dict(
|
114 |
-
dash="dash",
|
115 |
-
),
|
116 |
showlegend=True,
|
117 |
),
|
118 |
)
|
@@ -126,8 +151,8 @@ fig.update_layout(
|
|
126 |
overlaying="y",
|
127 |
side="right",
|
128 |
range=[0, 100],
|
129 |
-
tickmode="sync"
|
130 |
-
)
|
131 |
# template="plotly_dark",
|
132 |
)
|
133 |
|
@@ -137,8 +162,6 @@ st.plotly_chart(fig)
|
|
137 |
|
138 |
fig = go.Figure()
|
139 |
|
140 |
-
# df["yield"] = np.array(df["nproducts"]) / 128 * 100
|
141 |
-
|
142 |
df = df.sort_values("yield", ascending=True)
|
143 |
|
144 |
fig.add_trace(
|
@@ -173,7 +196,7 @@ fig.add_trace(
|
|
173 |
opacity=0.75,
|
174 |
orientation="h",
|
175 |
marker=dict(color=[method_color_mapping[method] for method in df["method"]]),
|
176 |
-
text=df["steps_per_second"].round(1)
|
177 |
)
|
178 |
)
|
179 |
|
@@ -184,3 +207,137 @@ fig.update_layout(
|
|
184 |
)
|
185 |
|
186 |
st.plotly_chart(fig)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
|
10 |
+
from mlip_arena.models import REGISTRY as MODELS
|
11 |
|
12 |
DATA_DIR = Path("mlip_arena/tasks/combustion")
|
13 |
|
|
|
18 |
|
19 |
st.markdown("### Methods")
|
20 |
container = st.container(border=True)
|
21 |
+
valid_models = [
|
22 |
+
model
|
23 |
+
for model, metadata in MODELS.items()
|
24 |
+
if Path(__file__).stem in metadata.get("gpu-tasks", [])
|
25 |
+
]
|
26 |
|
27 |
models = container.multiselect(
|
28 |
+
"MLIPs",
|
29 |
+
valid_models,
|
30 |
+
[
|
31 |
+
"MACE-MP(M)",
|
32 |
+
"CHGNet",
|
33 |
+
"M3GNet",
|
34 |
+
"SevenNet",
|
35 |
+
"ORB",
|
36 |
+
"EquiformerV2(OC22)",
|
37 |
+
"eSCN(OC20)",
|
38 |
+
],
|
39 |
)
|
40 |
|
41 |
st.markdown("### Settings")
|
|
|
51 |
|
52 |
palette_names = list(color_palettes.keys())
|
53 |
palette_colors = list(color_palettes.values())
|
|
|
54 |
palette_name = vis.selectbox("Color sequence", options=palette_names, index=22)
|
55 |
|
56 |
color_sequence = color_palettes[palette_name]
|
|
|
58 |
if not models:
|
59 |
st.stop()
|
60 |
|
|
|
61 |
|
62 |
+
@st.cache_data
|
63 |
+
def get_data(models):
|
64 |
+
families = [MODELS[str(model)]["family"] for model in models]
|
65 |
+
dfs = [
|
66 |
+
pd.read_json(DATA_DIR / family.lower() / "hydrogen.json") for family in families
|
67 |
+
]
|
68 |
+
df = pd.concat(dfs, ignore_index=True)
|
69 |
+
df.drop_duplicates(inplace=True, subset=["formula", "method"])
|
70 |
+
return df
|
71 |
+
|
72 |
+
|
73 |
+
df = get_data(models)
|
74 |
+
|
75 |
+
# families = [MODELS[str(model)]["family"] for model in models]
|
76 |
+
|
77 |
+
# dfs = [pd.read_json(DATA_DIR / family.lower() / "hydrogen.json") for family in families]
|
78 |
+
# df = pd.concat(dfs, ignore_index=True)
|
79 |
+
# df.drop_duplicates(inplace=True, subset=["formula", "method"])
|
80 |
|
81 |
method_color_mapping = {
|
82 |
method: color_sequence[i % len(color_sequence)]
|
|
|
121 |
y=row["temperatures"],
|
122 |
mode="markers",
|
123 |
name=method,
|
124 |
+
line=dict(
|
125 |
+
color=method_color_mapping[method],
|
126 |
+
# width=1
|
127 |
+
),
|
128 |
+
marker=dict(color=method_color_mapping[method], size=3),
|
129 |
showlegend=True,
|
130 |
),
|
131 |
)
|
|
|
133 |
target_steps = df["target_steps"].iloc[0]
|
134 |
fig.add_trace(
|
135 |
go.Line(
|
136 |
+
x=[0, target_steps / 3, target_steps / 3 * 2, target_steps],
|
137 |
y=[300, 3000, 3000, 300],
|
138 |
mode="lines",
|
139 |
name="Target",
|
140 |
+
line=dict(dash="dash", color="white"),
|
|
|
|
|
141 |
showlegend=True,
|
142 |
),
|
143 |
)
|
|
|
151 |
overlaying="y",
|
152 |
side="right",
|
153 |
range=[0, 100],
|
154 |
+
tickmode="sync",
|
155 |
+
),
|
156 |
# template="plotly_dark",
|
157 |
)
|
158 |
|
|
|
162 |
|
163 |
fig = go.Figure()
|
164 |
|
|
|
|
|
165 |
df = df.sort_values("yield", ascending=True)
|
166 |
|
167 |
fig.add_trace(
|
|
|
196 |
opacity=0.75,
|
197 |
orientation="h",
|
198 |
marker=dict(color=[method_color_mapping[method] for method in df["method"]]),
|
199 |
+
text=df["steps_per_second"].round(1),
|
200 |
)
|
201 |
)
|
202 |
|
|
|
207 |
)
|
208 |
|
209 |
st.plotly_chart(fig)
|
210 |
+
|
211 |
+
# COM drift
|
212 |
+
|
213 |
+
st.markdown("""### Center of mass drift
|
214 |
+
|
215 |
+
The center of mass (COM) drift is a measure of the stability of the simulation. A well-behaved simulation should have a COM drift close to zero. The COM drift is calculated as the displacement of the COM of the system from the initial position.
|
216 |
+
""")
|
217 |
+
|
218 |
+
|
219 |
+
@st.cache_data
|
220 |
+
def get_com_drifts(df):
|
221 |
+
df_exploded = df.explode(["timestep", "com_drifts"]).reset_index(drop=True)
|
222 |
+
|
223 |
+
# Convert the 'com_drifts' column (which are arrays) into separate columns for x, y, and z components
|
224 |
+
df_exploded[["com_drift_x", "com_drift_y", "com_drift_z"]] = pd.DataFrame(
|
225 |
+
df_exploded["com_drifts"].tolist(), index=df_exploded.index
|
226 |
+
)
|
227 |
+
|
228 |
+
# Drop the original 'com_drifts' column
|
229 |
+
df_flat = df_exploded.drop(columns=["com_drifts"])
|
230 |
+
|
231 |
+
df_flat["total_com_drift"] = np.sqrt(
|
232 |
+
df_flat["com_drift_x"] ** 2 + df_flat["com_drift_y"] ** 2 + df_flat["com_drift_z"] ** 2
|
233 |
+
)
|
234 |
+
|
235 |
+
return df_flat
|
236 |
+
|
237 |
+
|
238 |
+
df_exploded = get_com_drifts(df)
|
239 |
+
|
240 |
+
if "play" not in st.session_state:
|
241 |
+
st.session_state.play = False
|
242 |
+
|
243 |
+
|
244 |
+
def toggle_playing():
|
245 |
+
st.session_state.play = not st.session_state.play
|
246 |
+
|
247 |
+
|
248 |
+
st.button(
|
249 |
+
"Play" if not st.session_state.play else "Pause",
|
250 |
+
type="primary" if not st.session_state.play else "secondary",
|
251 |
+
on_click=toggle_playing,
|
252 |
+
)
|
253 |
+
|
254 |
+
increment = df["target_steps"].max() // 200
|
255 |
+
|
256 |
+
if "time_range" not in st.session_state:
|
257 |
+
st.session_state.time_range = (0, increment)
|
258 |
+
|
259 |
+
|
260 |
+
@st.fragment(run_every=1e-3 if st.session_state.play else None)
|
261 |
+
def draw_com_drifts_plot():
|
262 |
+
if st.session_state.play:
|
263 |
+
start, end = st.session_state.time_range
|
264 |
+
|
265 |
+
end += increment
|
266 |
+
|
267 |
+
if end > df["target_steps"].max():
|
268 |
+
start = 0
|
269 |
+
end = 0
|
270 |
+
|
271 |
+
st.session_state.time_range = (start, end)
|
272 |
+
|
273 |
+
start_timestep, end_timestep = st.slider(
|
274 |
+
"Timestep",
|
275 |
+
min_value=0,
|
276 |
+
max_value=df["target_steps"].max(),
|
277 |
+
value=st.session_state.time_range,
|
278 |
+
key="time_range",
|
279 |
+
# on_change=check_range,
|
280 |
+
)
|
281 |
+
|
282 |
+
mask = (df_exploded["timestep"] >= start_timestep) & (
|
283 |
+
df_exploded["timestep"] <= end_timestep
|
284 |
+
)
|
285 |
+
df_filtered = df_exploded[mask]
|
286 |
+
df_filtered.sort_values(["method", "timestep"], inplace=True)
|
287 |
+
|
288 |
+
fig = px.line_3d(
|
289 |
+
data_frame=df_filtered,
|
290 |
+
x="com_drift_x",
|
291 |
+
y="com_drift_y",
|
292 |
+
z="com_drift_z",
|
293 |
+
labels={"com_drift_x": "Δx (Å)", "com_drift_y": "Δy (Å)", "com_drift_z": "Δz (Å)"},
|
294 |
+
category_orders={"method": df_exploded["method"].unique()},
|
295 |
+
color_discrete_sequence=[
|
296 |
+
method_color_mapping[method] for method in df_exploded["method"].unique()
|
297 |
+
],
|
298 |
+
color="method",
|
299 |
+
width=800,
|
300 |
+
height=800,
|
301 |
+
)
|
302 |
+
|
303 |
+
fig.update_layout(
|
304 |
+
scene=dict(
|
305 |
+
aspectmode="cube",
|
306 |
+
),
|
307 |
+
legend=dict(
|
308 |
+
orientation="v",
|
309 |
+
x=0.95,
|
310 |
+
xanchor="right",
|
311 |
+
y=1,
|
312 |
+
yanchor="top",
|
313 |
+
bgcolor="rgba(0, 0, 0, 0)",
|
314 |
+
),
|
315 |
+
)
|
316 |
+
fig.add_traces([
|
317 |
+
go.Scatter3d(
|
318 |
+
x=[0],
|
319 |
+
y=[0],
|
320 |
+
z=[0],
|
321 |
+
mode="markers",
|
322 |
+
marker=dict(size=2, color="white"),
|
323 |
+
name="Origin",
|
324 |
+
),
|
325 |
+
# add last point of each method and annotate the total drift
|
326 |
+
go.Scatter3d(
|
327 |
+
# df_filtered.groupby("method")["com_drift_x"].last(),
|
328 |
+
x=df_filtered.groupby("method")["com_drift_x"].last(),
|
329 |
+
y=df_filtered.groupby("method")["com_drift_y"].last(),
|
330 |
+
z=df_filtered.groupby("method")["com_drift_z"].last(),
|
331 |
+
mode="markers+text",
|
332 |
+
marker=dict(size=2, color="rgba(0, 0, 0, 128)"),
|
333 |
+
text=df_filtered.groupby("method")["total_com_drift"].last().round(3),
|
334 |
+
# size=5,
|
335 |
+
name="total drifts",
|
336 |
+
textposition="top center",
|
337 |
+
),
|
338 |
+
])
|
339 |
+
|
340 |
+
st.plotly_chart(fig)
|
341 |
+
|
342 |
+
|
343 |
+
draw_com_drifts_plot()
|
serve/tasks/homonuclear-diatomics.py
CHANGED
@@ -60,18 +60,23 @@ DATA_DIR = Path("mlip_arena/tasks/diatomics")
|
|
60 |
if not mlip_methods and not dft_methods:
|
61 |
st.stop()
|
62 |
|
63 |
-
|
64 |
-
|
65 |
-
|
66 |
-
]
|
67 |
-
|
68 |
-
[
|
69 |
-
pd.read_json(DATA_DIR / method.lower() / "homonuclear-diatomics.json")
|
70 |
-
for method in dft_methods
|
71 |
]
|
72 |
-
|
73 |
-
|
74 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
75 |
|
76 |
method_color_mapping = {
|
77 |
method: color_sequence[i % len(color_sequence)]
|
@@ -81,133 +86,152 @@ method_color_mapping = {
|
|
81 |
# img_dir = Path('./images')
|
82 |
# img_dir.mkdir(exist_ok=True)
|
83 |
|
84 |
-
for i, symbol in enumerate(chemical_symbols[1:]):
|
85 |
-
if i % ncols == 0:
|
86 |
-
cols = st.columns(ncols)
|
87 |
|
88 |
-
|
|
|
89 |
|
90 |
-
|
91 |
-
continue
|
92 |
|
93 |
-
|
|
|
94 |
|
95 |
-
|
96 |
|
97 |
-
|
98 |
-
if method not in mlip_methods and method not in dft_methods:
|
99 |
continue
|
100 |
-
row = rows[rows["method"] == method].iloc[0]
|
101 |
|
102 |
-
|
103 |
-
es = np.array(row["E"])
|
104 |
-
fs = np.array(row["F"])
|
105 |
|
106 |
-
|
107 |
-
ind = np.argsort(rs)
|
108 |
-
es = np.array(es)
|
109 |
-
fs = np.array(fs)
|
110 |
|
111 |
-
|
112 |
-
|
113 |
-
|
114 |
-
|
115 |
-
else:
|
116 |
-
pass
|
117 |
|
118 |
-
|
119 |
-
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|
120 |
|
121 |
-
|
122 |
-
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123 |
-
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124 |
-
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|
125 |
|
126 |
-
if energy_plot:
|
127 |
if "GPAW" in method:
|
128 |
-
|
129 |
-
ys = cs(xs)
|
130 |
else:
|
131 |
-
|
132 |
-
|
133 |
-
|
134 |
-
|
135 |
-
|
136 |
-
|
137 |
-
|
138 |
-
|
139 |
-
|
140 |
-
|
141 |
-
|
142 |
-
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|
143 |
),
|
144 |
-
|
145 |
-
)
|
146 |
-
|
147 |
-
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|
148 |
|
149 |
-
|
150 |
-
ys = fs
|
151 |
|
152 |
-
|
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|
153 |
|
154 |
-
|
155 |
-
|
156 |
-
|
157 |
-
|
158 |
-
|
159 |
-
|
160 |
-
|
161 |
-
|
162 |
-
|
163 |
-
),
|
164 |
-
|
165 |
-
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|
166 |
),
|
167 |
-
secondary_y=True,
|
168 |
)
|
169 |
|
170 |
-
name = f"{symbol}-{symbol}"
|
171 |
-
|
172 |
-
fig.update_layout(
|
173 |
-
showlegend=True,
|
174 |
-
legend=dict(
|
175 |
-
orientation="v",
|
176 |
-
x=0.95,
|
177 |
-
xanchor="right",
|
178 |
-
y=1,
|
179 |
-
yanchor="top",
|
180 |
-
bgcolor="rgba(0, 0, 0, 0)"
|
181 |
-
# entrywidth=0.3,
|
182 |
-
# entrywidthmode='fraction',
|
183 |
-
),
|
184 |
-
title_text=f"{name}",
|
185 |
-
title_x=0.5,
|
186 |
-
)
|
187 |
|
188 |
-
|
189 |
-
fig.update_xaxes(title_text="Distance [Å]")
|
190 |
|
191 |
-
|
192 |
-
|
193 |
-
|
194 |
-
|
195 |
-
title=dict(text="Energy [eV]"),
|
196 |
-
side="left",
|
197 |
-
range=[elo, 1.5 * (abs(elo))],
|
198 |
-
)
|
199 |
-
)
|
200 |
|
201 |
-
if force_plot:
|
202 |
-
fig.update_layout(
|
203 |
-
yaxis2=dict(
|
204 |
-
title=dict(text="Force [eV/Å]"),
|
205 |
-
side="right",
|
206 |
-
range=[flo, 1.0 * abs(flo)],
|
207 |
-
overlaying="y",
|
208 |
-
tickmode="sync",
|
209 |
-
),
|
210 |
-
)
|
211 |
|
|
|
|
|
|
|
|
|
|
|
212 |
cols[i % ncols].plotly_chart(fig, use_container_width=True)
|
213 |
-
|
|
|
60 |
if not mlip_methods and not dft_methods:
|
61 |
st.stop()
|
62 |
|
63 |
+
@st.cache_data
|
64 |
+
def get_data(mlip_methods, dft_methods):
|
65 |
+
dfs = [
|
66 |
+
pd.read_json(DATA_DIR / REGISTRY[method]["family"] / "homonuclear-diatomics.json")
|
67 |
+
for method in mlip_methods
|
|
|
|
|
|
|
68 |
]
|
69 |
+
dfs.extend(
|
70 |
+
[
|
71 |
+
pd.read_json(DATA_DIR / method.lower() / "homonuclear-diatomics.json")
|
72 |
+
for method in dft_methods
|
73 |
+
]
|
74 |
+
)
|
75 |
+
df = pd.concat(dfs, ignore_index=True)
|
76 |
+
df.drop_duplicates(inplace=True, subset=["name", "method"])
|
77 |
+
return df
|
78 |
+
|
79 |
+
df = get_data(mlip_methods, dft_methods)
|
80 |
|
81 |
method_color_mapping = {
|
82 |
method: color_sequence[i % len(color_sequence)]
|
|
|
86 |
# img_dir = Path('./images')
|
87 |
# img_dir.mkdir(exist_ok=True)
|
88 |
|
|
|
|
|
|
|
89 |
|
90 |
+
@st.cache_data
|
91 |
+
def get_plots(df, energy_plot, force_plot):
|
92 |
|
93 |
+
figs = []
|
|
|
94 |
|
95 |
+
for i, symbol in enumerate(chemical_symbols[1:]):
|
96 |
+
|
97 |
|
98 |
+
rows = df[df["name"] == symbol + symbol]
|
99 |
|
100 |
+
if rows.empty:
|
|
|
101 |
continue
|
|
|
102 |
|
103 |
+
fig = make_subplots(specs=[[{"secondary_y": True}]])
|
|
|
|
|
104 |
|
105 |
+
elo, flo = float("inf"), float("inf")
|
|
|
|
|
|
|
106 |
|
107 |
+
for j, method in enumerate(rows["method"].unique()):
|
108 |
+
if method not in mlip_methods and method not in dft_methods:
|
109 |
+
continue
|
110 |
+
row = rows[rows["method"] == method].iloc[0]
|
|
|
|
|
111 |
|
112 |
+
rs = np.array(row["R"])
|
113 |
+
es = np.array(row["E"])
|
114 |
+
fs = np.array(row["F"])
|
115 |
|
116 |
+
rs = np.array(rs)
|
117 |
+
ind = np.argsort(rs)
|
118 |
+
es = np.array(es)
|
119 |
+
fs = np.array(fs)
|
120 |
+
|
121 |
+
rs = rs[ind]
|
122 |
+
es = es[ind]
|
123 |
+
if "GPAW" not in method:
|
124 |
+
es = es - es[-1]
|
125 |
+
else:
|
126 |
+
pass
|
127 |
+
|
128 |
+
if "GPAW" not in method:
|
129 |
+
fs = fs[ind]
|
130 |
|
|
|
131 |
if "GPAW" in method:
|
132 |
+
xs = np.linspace(rs.min() * 0.99, rs.max() * 1.01, int(5e2))
|
|
|
133 |
else:
|
134 |
+
xs = rs
|
135 |
+
|
136 |
+
if energy_plot:
|
137 |
+
if "GPAW" in method:
|
138 |
+
cs = CubicSpline(rs, es)
|
139 |
+
ys = cs(xs)
|
140 |
+
else:
|
141 |
+
ys = es
|
142 |
+
|
143 |
+
elo = min(elo, max(ys.min() * 1.2, -15), -1)
|
144 |
+
|
145 |
+
fig.add_trace(
|
146 |
+
go.Scatter(
|
147 |
+
x=xs,
|
148 |
+
y=ys,
|
149 |
+
mode="lines",
|
150 |
+
line=dict(
|
151 |
+
color=method_color_mapping[method],
|
152 |
+
width=3,
|
153 |
+
),
|
154 |
+
name=method,
|
155 |
),
|
156 |
+
secondary_y=False,
|
157 |
+
)
|
158 |
+
|
159 |
+
if force_plot and "GPAW" not in method:
|
160 |
+
ys = fs
|
161 |
+
|
162 |
+
flo = min(flo, max(ys.min() * 1.2, -50))
|
163 |
+
|
164 |
+
fig.add_trace(
|
165 |
+
go.Scatter(
|
166 |
+
x=xs,
|
167 |
+
y=ys,
|
168 |
+
mode="lines",
|
169 |
+
line=dict(
|
170 |
+
color=method_color_mapping[method],
|
171 |
+
width=2,
|
172 |
+
dash="dashdot",
|
173 |
+
),
|
174 |
+
name=method,
|
175 |
+
showlegend=not energy_plot,
|
176 |
+
),
|
177 |
+
secondary_y=True,
|
178 |
+
)
|
179 |
|
180 |
+
name = f"{symbol}-{symbol}"
|
|
|
181 |
|
182 |
+
fig.update_layout(
|
183 |
+
showlegend=True,
|
184 |
+
legend=dict(
|
185 |
+
orientation="v",
|
186 |
+
x=0.95,
|
187 |
+
xanchor="right",
|
188 |
+
y=1,
|
189 |
+
yanchor="top",
|
190 |
+
bgcolor="rgba(0, 0, 0, 0)"
|
191 |
+
# entrywidth=0.3,
|
192 |
+
# entrywidthmode='fraction',
|
193 |
+
),
|
194 |
+
title_text=f"{name}",
|
195 |
+
title_x=0.5,
|
196 |
+
)
|
197 |
|
198 |
+
# Set x-axis title
|
199 |
+
fig.update_xaxes(title_text="Distance [Å]")
|
200 |
+
|
201 |
+
# Set y-axes titles
|
202 |
+
if energy_plot:
|
203 |
+
fig.update_layout(
|
204 |
+
yaxis=dict(
|
205 |
+
title=dict(text="Energy [eV]"),
|
206 |
+
side="left",
|
207 |
+
range=[elo, 1.5 * (abs(elo))],
|
208 |
+
)
|
209 |
+
)
|
210 |
+
|
211 |
+
if force_plot:
|
212 |
+
fig.update_layout(
|
213 |
+
yaxis2=dict(
|
214 |
+
title=dict(text="Force [eV/Å]"),
|
215 |
+
side="right",
|
216 |
+
range=[flo, 1.0 * abs(flo)],
|
217 |
+
overlaying="y",
|
218 |
+
tickmode="sync",
|
219 |
),
|
|
|
220 |
)
|
221 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
222 |
|
223 |
+
# cols[i % ncols].plotly_chart(fig, use_container_width=True)
|
|
|
224 |
|
225 |
+
figs.append(fig)
|
226 |
+
|
227 |
+
return figs
|
228 |
+
# fig.write_image(format='svg', file=img_dir / f"{name}.svg")
|
|
|
|
|
|
|
|
|
|
|
229 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
230 |
|
231 |
+
figs = get_plots(df, energy_plot, force_plot)
|
232 |
+
|
233 |
+
for i, fig in enumerate(figs):
|
234 |
+
if i % ncols == 0:
|
235 |
+
cols = st.columns(ncols)
|
236 |
cols[i % ncols].plotly_chart(fig, use_container_width=True)
|
237 |
+
|