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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare input structures\n",
"\n",
"We first start by preparing the input ASE database. In the subfolder `structures` there are 9 example MOF structures. `save_to_db` is an convenient function to conver CIF files into one db."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
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"outputs": [
{
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"text": [
"\u001b[32m2024-12-22 11:40:02.836\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mmlip_arena.tasks.mof\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m6\u001b[0m - \u001b[1m\n",
"The module 'mlip_arena.tasks.mof' is adapted from the repository: https://github.com/hspark1212/DAC-SIM. \n",
"By using this module, you agree to the terms and conditions specified in the following license: \n",
"\n",
"https://github.com/hspark1212/DAC-SIM/blob/main/LICENSE\n",
"\n",
"Additionally, please ensure proper attribution by citing the reference: \n",
"\n",
"Lim, Y., Park, H., Walsh, A., & Kim, J. (2024). Accelerating CO₂ Direct Air Capture Screening for Metal-Organic Frameworks with a Transferable Machine Learning Force Field.\n",
"\n",
"A local copy of the LICENSE file can be found at: /pscratch/sd/c/cyrusyc/mlip-arena/mlip_arena/tasks/mof/LICENSE.\n",
"\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"No module named 'deepmd'\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/ase/io/cif.py:408: UserWarning: crystal system 'triclinic' is not interpreted for space group Spacegroup(1, setting=1). This may result in wrong setting!\n",
" warnings.warn(\n",
"\u001b[32m2024-12-22 11:40:22.686\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mmlip_arena.tasks.mof.input\u001b[0m:\u001b[36msave_to_db\u001b[0m:\u001b[36m45\u001b[0m - \u001b[1mmofs.db uploaded to atomind/mlip-arena/mof\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"PosixPath('mofs.db')"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dotenv import load_dotenv\n",
"load_dotenv()\n",
"\n",
"import glob\n",
"from ase.io import read\n",
"from mlip_arena.tasks.mof.input import save_to_db\n",
"\n",
"files = glob.glob('structures/*.cif')\n",
"\n",
"atoms_list = []\n",
"\n",
"for file in files:\n",
" atoms = read(file)\n",
" atoms_list.append(atoms)\n",
"\n",
"save_to_db(\n",
" atoms_list=atoms_list,\n",
" db_path='mofs.db'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## Demo: MOF benchmark workflow"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from ase.build import molecule\n",
"from mlip_arena.models import MLIPEnum\n",
"from mlip_arena.tasks.mof.input import get_atoms_from_db\n",
"from mlip_arena.tasks.mof.flow import widom_insertion\n",
"from prefect import flow\n",
"from tqdm.auto import tqdm\n",
"\n",
"@flow\n",
"def benchmark_one():\n",
" results = []\n",
" for model in MLIPEnum:\n",
" if model.name != \"MACE-MP(M)\":\n",
" continue # skip all models other than MACE-MP\n",
" for atoms in tqdm(get_atoms_from_db('mofs.db')):\n",
" result = widom_insertion(\n",
" atoms, \n",
" molecule('CO2'),\n",
" calculator_name=model.name,\n",
" )\n",
" results.append(result)\n",
" break # only test on first MOF\n",
" return [r for r in results]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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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\">15:46:06.786 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'amigurumi-beagle'</span> - Beginning flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'amigurumi-beagle'</span> for flow<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> 'benchmark-one'</span>\n",
"</pre>\n"
],
"text/plain": [
"15:46:06.786 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'amigurumi-beagle'\u001b[0m - Beginning flow run\u001b[35m 'amigurumi-beagle'\u001b[0m for flow\u001b[1;35m 'benchmark-one'\u001b[0m\n"
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"0it [00:00, ?it/s]"
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"mof/mofs.db: 0%| | 0.00/168k [00:00<?, ?B/s]"
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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\">15:46:07.619 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Task run 'Widom Insertion: C28H16O10V2 + CO2 - MACE-MP(M)' - Optimizing structure\n",
"</pre>\n"
],
"text/plain": [
"15:46:07.619 | \u001b[36mINFO\u001b[0m | Task run 'Widom Insertion: C28H16O10V2 + CO2 - MACE-MP(M)' - Optimizing structure\n"
]
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"text": [
"Selected GPU cuda:0 with 40339.31 MB free memory from 1 GPUs\n",
"Using device: cuda:0\n",
"Selected GPU cuda:0 with 40339.31 MB free memory from 1 GPUs\n",
"Default dtype float32 does not match model dtype float64, converting models to float32.\n",
"Using calculator: <mlip_arena.models.externals.mace-mp.MACE_MP_Medium object at 0x7f8395425890>\n",
"Using filter: <ase.filters.FrechetCellFilter object at 0x7f839420ba50>\n",
"Using optimizer: <ase.optimize.fire2.FIRE2 object at 0x7f839662e4d0>\n",
" Step Time Energy fmax\n",
"FIRE2: 0 15:46:10 -398.611542 0.341164\n",
"FIRE2: 1 15:46:10 -398.627014 0.185914\n",
"FIRE2: 2 15:46:12 -398.632233 0.157950\n",
"FIRE2: 3 15:46:12 -398.636993 0.163124\n",
"FIRE2: 4 15:46:12 -398.633881 0.158292\n",
"FIRE2: 5 15:46:13 -398.642792 0.152467\n",
"FIRE2: 6 15:46:13 -398.643768 0.142569\n",
"FIRE2: 7 15:46:13 -398.637024 0.131079\n",
"FIRE2: 8 15:46:13 -398.648560 0.115860\n",
"FIRE2: 9 15:46:13 -398.648590 0.099157\n",
"FIRE2: 10 15:46:13 -398.654022 0.081072\n",
"FIRE2: 11 15:46:14 -398.651306 0.062052\n",
"FIRE2: 12 15:46:14 -398.661194 0.044053\n"
]
},
{
"data": {
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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\">15:46:14.836 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Task run 'OPT: C28H16O10V2 - MACE-MP(M)' - Finished in state <span style=\"color: #008000; text-decoration-color: #008000\">Completed</span>()\n",
"</pre>\n"
],
"text/plain": [
"15:46:14.836 | \u001b[36mINFO\u001b[0m | Task run 'OPT: C28H16O10V2 - MACE-MP(M)' - Finished in state \u001b[32mCompleted\u001b[0m()\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\">15:46:14.840 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Task run 'Widom Insertion: C28H16O10V2 + CO2 - MACE-MP(M)' - Optimizing gas molecule\n",
"</pre>\n"
],
"text/plain": [
"15:46:14.840 | \u001b[36mINFO\u001b[0m | Task run 'Widom Insertion: C28H16O10V2 + CO2 - MACE-MP(M)' - Optimizing gas molecule\n"
]
},
"metadata": {},
"output_type": "display_data"
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{
"name": "stdout",
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"text": [
"Selected GPU cuda:0 with 40301.98 MB free memory from 1 GPUs\n",
"Using device: cuda:0\n",
"Selected GPU cuda:0 with 40301.98 MB free memory from 1 GPUs\n",
"Default dtype float32 does not match model dtype float64, converting models to float32.\n",
"Using calculator: <mlip_arena.models.externals.mace-mp.MACE_MP_Medium object at 0x7f83942934d0>\n",
"Using optimizer: <ase.optimize.fire2.FIRE2 object at 0x7f8394175990>\n",
" Step Time Energy fmax\n",
"FIRE2: 0 15:46:15 -22.777348 0.437851\n",
"FIRE2: 1 15:46:17 -22.778408 0.014392\n"
]
},
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"data": {
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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\">15:46:17.127 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Task run 'OPT: CO2 - MACE-MP(M)' - Finished in state <span style=\"color: #008000; text-decoration-color: #008000\">Completed</span>()\n",
"</pre>\n"
],
"text/plain": [
"15:46:17.127 | \u001b[36mINFO\u001b[0m | Task run 'OPT: CO2 - MACE-MP(M)' - Finished in state \u001b[32mCompleted\u001b[0m()\n"
]
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"name": "stdout",
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"text": [
"Number of accessible positions: 357364 out of total 498623\n",
"Selected GPU cuda:0 with 40280.80 MB free memory from 1 GPUs\n",
"Using device: cuda:0\n",
"Selected GPU cuda:0 with 40280.80 MB free memory from 1 GPUs\n",
"Default dtype float32 does not match model dtype float64, converting models to float32.\n",
"Using calculator: <mlip_arena.models.externals.mace-mp.MACE_MP_Medium object at 0x7f838c597e50>\n"
]
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"version_minor": 0
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"text/plain": [
"Fold 1/3: 0%| | 0/5000 [00:00<?, ?it/s]"
]
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"text/plain": [
"Fold 2/3: 0%| | 0/5000 [00:00<?, ?it/s]"
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"version_minor": 0
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"text/plain": [
"Fold 3/3: 0%| | 0/5000 [00:00<?, ?it/s]"
]
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/pscratch/sd/c/cyrusyc/mlip-arena-dev/mlip_arena/tasks/mof/flow.py:291: RuntimeWarning: overflow encountered in exp\n",
" boltzmann_factor = np.exp(\n",
"/pscratch/sd/c/cyrusyc/mlip-arena-dev/mlip_arena/tasks/mof/flow.py:311: RuntimeWarning: overflow encountered in exp\n",
" boltzmann_factor = np.exp(\n",
"/pscratch/sd/c/cyrusyc/mlip-arena-dev/mlip_arena/tasks/mof/flow.py:326: RuntimeWarning: invalid value encountered in scalar divide\n",
" u = (interaction_energies * boltzmann_factor).sum() / boltzmann_factor.sum()\n"
]
},
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"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">15:52:13.884 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Task run 'Widom Insertion: C28H16O10V2 + CO2 - MACE-MP(M)' - Finished in state <span style=\"color: #008000; text-decoration-color: #008000\">Completed</span>()\n",
"</pre>\n"
],
"text/plain": [
"15:52:13.884 | \u001b[36mINFO\u001b[0m | Task run 'Widom Insertion: C28H16O10V2 + CO2 - MACE-MP(M)' - Finished in state \u001b[32mCompleted\u001b[0m()\n"
]
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"data": {
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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\">15:52:14.235 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'amigurumi-beagle'</span> - Finished in state <span style=\"color: #008000; text-decoration-color: #008000\">Completed</span>()\n",
"</pre>\n"
],
"text/plain": [
"15:52:14.235 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'amigurumi-beagle'\u001b[0m - Finished in state \u001b[32mCompleted\u001b[0m()\n"
]
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"data": {
"text/plain": [
"[2.3816888372250245e-06, 2.5323794093995965e-06, inf]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result = benchmark_one()\n",
"result[0]['henry_coefficient']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run workflow"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [
{
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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\">11:40:36.644 | <span style=\"color: #d7d700; text-decoration-color: #d7d700\">WARNING</span> | MDAnalysis.coordinates.AMBER - netCDF4 is not available. Writing AMBER ncdf files will be slow.\n",
"</pre>\n"
],
"text/plain": [
"11:40:36.644 | \u001b[38;5;184mWARNING\u001b[0m | MDAnalysis.coordinates.AMBER - netCDF4 is not available. Writing AMBER ncdf files will be slow.\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\">11:40:44.431 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | distributed.http.proxy - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n",
"</pre>\n"
],
"text/plain": [
"11:40:44.431 | \u001b[36mINFO\u001b[0m | distributed.http.proxy - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\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\">11:40:44.445 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | distributed.scheduler - State start\n",
"</pre>\n"
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"11:40:44.445 | \u001b[36mINFO\u001b[0m | distributed.scheduler - State start\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\">11:40:44.503 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | distributed.scheduler - Scheduler at: tcp://128.55.64.42:36351\n",
"</pre>\n"
],
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"11:40:44.503 | \u001b[36mINFO\u001b[0m | distributed.scheduler - Scheduler at: tcp://128.55.64.42:36351\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\">11:40:44.505 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | distributed.scheduler - dashboard at: <span style=\"color: #0000ff; text-decoration-color: #0000ff\">http://128.55.64.42:8787/status</span>\n",
"</pre>\n"
],
"text/plain": [
"11:40:44.505 | \u001b[36mINFO\u001b[0m | distributed.scheduler - dashboard at: \u001b[94mhttp://128.55.64.42:8787/status\u001b[0m\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\">11:40:44.506 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | distributed.scheduler - Registering Worker plugin shuffle\n",
"</pre>\n"
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"11:40:44.506 | \u001b[36mINFO\u001b[0m | distributed.scheduler - Registering Worker plugin shuffle\n"
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"text": [
"#!/bin/bash\n",
"\n",
"#SBATCH -A matgen\n",
"#SBATCH --mem=0\n",
"#SBATCH -t 00:30:00\n",
"#SBATCH -J mof\n",
"#SBATCH -q regular\n",
"#SBATCH -N 1\n",
"#SBATCH -C gpu\n",
"#SBATCH -G 4\n",
"source ~/.bashrc\n",
"module load python\n",
"source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\n",
"/pscratch/sd/c/cyrusyc/.conda/mlip-arena/bin/python -m distributed.cli.dask_worker tcp://128.55.64.42:36351 --name dummy-name --nthreads 1 --memory-limit 59.60GiB --nanny --death-timeout 60\n",
"\n"
]
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"</pre>\n"
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"11:40:44.514 | \u001b[36mINFO\u001b[0m | distributed.deploy.adaptive - Adaptive scaling started: minimum=10 maximum=20\n"
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"11:40:45.048 | \u001b[36mINFO\u001b[0m | prefect.engine - View at \u001b[94mhttps://app.prefect.cloud/account/f7d40474-9362-4bfa-8950-ee6a43ec00f3/workspace/d4bb0913-5f5e-49f7-bfc5-06509088baeb/runs/flow-run/c0c7a3f2-d8d0-4f17-9789-4e070f17bf3b\u001b[0m\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\">11:40:45.366 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | prefect.task_runner.dask - Connecting to existing Dask cluster SLURMCluster(00ac1d39, 'tcp://128.55.64.42:36351', workers=0, threads=0, memory=0 B)\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\">11:40:45.501 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Task run 'get_atoms_from_db-6be' - Created task run 'get_atoms_from_db-6be' for task 'get_atoms_from_db'\n",
"</pre>\n"
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"11:40:45.501 | \u001b[36mINFO\u001b[0m | Task run 'get_atoms_from_db-6be' - Created task run 'get_atoms_from_db-6be' for task 'get_atoms_from_db'\n"
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],
"source": [
"from dask.distributed import Client\n",
"from dask_jobqueue import SLURMCluster\n",
"from prefect_dask import DaskTaskRunner\n",
"from mlip_arena.tasks.mof.flow import run as MOF\n",
"\n",
"# Orchestrate your awesome dask workflow runner\n",
"\n",
"nodes_per_alloc = 1\n",
"gpus_per_alloc = 4\n",
"ntasks = 1\n",
"\n",
"cluster_kwargs = dict(\n",
" cores=1,\n",
" memory=\"64 GB\",\n",
" shebang=\"#!/bin/bash\",\n",
" account=\"matgen\",\n",
" walltime=\"00:30:00\",\n",
" job_mem=\"0\",\n",
" job_script_prologue=[\n",
" \"source ~/.bashrc\",\n",
" \"module load python\",\n",
" \"source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\",\n",
" ],\n",
" job_directives_skip=[\"-n\", \"--cpus-per-task\", \"-J\"],\n",
" job_extra_directives=[\n",
" \"-J mof\",\n",
" \"-q regular\",\n",
" f\"-N {nodes_per_alloc}\",\n",
" \"-C gpu\",\n",
" f\"-G {gpus_per_alloc}\",\n",
" ],\n",
")\n",
"\n",
"cluster = SLURMCluster(**cluster_kwargs)\n",
"print(cluster.job_script())\n",
"cluster.adapt(minimum_jobs=10, maximum_jobs=20)\n",
"client = Client(cluster)\n",
"\n",
"# Run the workflow on HPC cluster in parallel\n",
"\n",
"results = MOF.with_options(\n",
" task_runner=DaskTaskRunner(address=client.scheduler.address),\n",
" # log_prints=True,\n",
")()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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},
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