新建
Instructions for OpenLAM structure optimization tools
zjgemi
更新于 2024-11-20
推荐镜像 :lam-crystal-philately:openlam
推荐机型 :c12_m92_1 * NVIDIA V100
赞
目录
数据集
lam-crystal-model(v3)
Installation
Clone the project to the local
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[1]
!git clone https://github.com/deepmodeling/openlam
Cloning into 'openlam'... remote: Enumerating objects: 103, done. remote: Counting objects: 100% (103/103), done. remote: Compressing objects: 100% (73/73), done. remote: Total 103 (delta 52), reused 68 (delta 26), pack-reused 0 (from 0) Receiving objects: 100% (103/103), 33.91 KiB | 394.00 KiB/s, done. Resolving deltas: 100% (52/52), done.
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Install it with dependencies
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[2]
!pip install ./openlam
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Processing /openlam Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting pydflow>=1.7.23 (from lam-crystal-philately==0.1.dev23+g7d4fc12) Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6d/b7/2c067e6d5e7a274eb449418948d772c5cb692ddea53fbbec15c97958bb73/pydflow-1.8.85-py3-none-any.whl (163 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 163.1/163.1 kB 14.2 MB/s eta 0:00:00 Collecting ase==3.22.1 (from lam-crystal-philately==0.1.dev23+g7d4fc12) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/38/b0/3c0a7afaf66274588216c251376ac2bea0269eb7a5e1da77521811060553/ase-3.22.1-py3-none-any.whl (2.2 MB) Requirement already satisfied: numpy==1.26.4 in /opt/mamba/envs/matbench/lib/python3.11/site-packages (from lam-crystal-philately==0.1.dev23+g7d4fc12) (1.26.4) Requirement already satisfied: tqdm==4.66.2 in /opt/mamba/envs/matbench/lib/python3.11/site-packages (from lam-crystal-philately==0.1.dev23+g7d4fc12) (4.66.2) Collecting pymatgen==2024.3.1 (from lam-crystal-philately==0.1.dev23+g7d4fc12) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/73/43/f7e3b8ab721c488a8b7321012a6585977d638630e4469c8e47644982db58/pymatgen-2024.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.1 MB) Requirement already satisfied: matplotlib>=3.1.0 in /opt/mamba/envs/matbench/lib/python3.11/site-packages (from ase==3.22.1->lam-crystal-philately==0.1.dev23+g7d4fc12) (3.8.3) Requirement already satisfied: scipy>=1.1.0 in /opt/mamba/envs/matbench/lib/python3.11/site-packages (from ase==3.22.1->lam-crystal-philately==0.1.dev23+g7d4fc12) (1.12.0) Requirement already satisfied: monty>=2024.2.2 in /opt/mamba/envs/matbench/lib/python3.11/site-packages (from pymatgen==2024.3.1->lam-crystal-philately==0.1.dev23+g7d4fc12) (2024.2.26) Requirement already satisfied: networkx>=2.2 in 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/tmp/pip-ephem-wheel-cache-hzsquqgd/wheels/61/47/de/ef330879f8aedf68b464ac99bcc69a236487ada5c119f4671e Successfully built lam-crystal-philately Installing collected packages: argo-workflows, websocket-client, jsonpickle, cloudpickle, argon2-cffi-bindings, pymatgen, kubernetes, ase, argon2-cffi, minio, pydflow, lam-crystal-philately Attempting uninstall: pymatgen Found existing installation: pymatgen 2024.4.13 Uninstalling pymatgen-2024.4.13: Successfully uninstalled pymatgen-2024.4.13 Attempting uninstall: ase Found existing installation: ase 3.23.0b1 Uninstalling ase-3.23.0b1: Successfully uninstalled ase-3.23.0b1 Successfully installed argo-workflows-5.0.0 argon2-cffi-23.1.0 argon2-cffi-bindings-21.2.0 ase-3.22.1 cloudpickle-2.2.0 jsonpickle-3.2.2 kubernetes-30.1.0 lam-crystal-philately-0.1.dev23+g7d4fc12 minio-7.2.8 pydflow-1.8.85 pymatgen-2024.3.1 websocket-client-1.8.0 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
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A DP model exists in the dataset of this notebook, soft-link it to current directory
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[1]
!ln -s /bohr/lam-crystal-model-01oe/v3/dp0529.pth dp.pth
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Optimize Structures Locally
Optimize example structures locally using commandline tool.
After structures are optimized, their convergence (skip with argument --skip-check-convergence) and duplicate in OpenLAM Database or not (skip with argument --skip-check-duplicate) will be checked by default. The latter check requires environmental variable BOHRIUM_ACCESS_KEY, which can be generated from https://bohrium.dp.tech/settings/user
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%env BOHRIUM_ACCESS_KEY=xxx
env: BOHRIUM_ACCESS_KEY=xxx
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!lam-opt relax -i openlam/examples/data -m dp.pth -o results.json --skip-check-convergence
To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, DP_INTRA_OP_PARALLELISM_THREADS, and DP_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information. Start to relax structures. Relaxing: 0it [00:00, ?it/s] Step Time Energy fmax BFGS: 0 11:59:39 -3.432330 1.4856 BFGS: 1 11:59:42 -3.462572 1.2149 BFGS: 2 11:59:52 -3.474138 1.2986 BFGS: 3 11:59:56 -3.499632 0.2218 BFGS: 4 11:59:56 -3.500611 0.0249 BFGS: 5 11:59:56 -3.500631 0.0114 BFGS: 6 11:59:56 -3.500635 0.0110 BFGS: 7 11:59:56 -3.500739 0.0496 BFGS: 8 11:59:57 -3.500918 0.0937 BFGS: 9 11:59:57 -3.501440 0.1756 BFGS: 10 11:59:57 -3.502467 0.2799 BFGS: 11 11:59:57 -3.504224 0.3837 BFGS: 12 11:59:57 -3.540719 0.7027 BFGS: 13 11:59:57 -3.545436 0.2616 BFGS: 14 11:59:57 -3.546308 0.1420 BFGS: 15 11:59:57 -3.546664 0.0037 BFGS: 16 11:59:57 -3.546664 0.0000 Relaxing: 1it [00:22, 22.67s/it] Step Time Energy fmax BFGS: 0 11:59:57 -5.405985 0.7214 BFGS: 1 12:00:07 -5.421183 0.7691 BFGS: 2 12:00:07 -5.421177 2.1633 BFGS: 3 12:00:07 -5.503029 0.4127 BFGS: 4 12:00:07 -5.515699 0.5083 BFGS: 5 12:00:07 -5.550243 0.5736 BFGS: 6 12:00:07 -5.302219 3.1964 BFGS: 7 12:00:08 -5.563675 0.8995 BFGS: 8 12:00:08 -5.438606 0.7443 BFGS: 9 12:00:08 -5.545653 0.3734 BFGS: 10 12:00:08 -5.563606 1.3026 BFGS: 11 12:00:08 -5.564468 0.3407 BFGS: 12 12:00:08 -5.577429 0.3208 BFGS: 13 12:00:08 -5.247499 2.8527 BFGS: 14 12:00:08 -5.588167 0.5249 BFGS: 15 12:00:08 -5.569788 0.3426 BFGS: 16 12:00:08 -5.586407 0.2904 BFGS: 17 12:00:08 -5.291737 2.2892 BFGS: 18 12:00:08 -5.568543 1.3995 BFGS: 19 12:00:09 -5.467279 0.7000 BFGS: 20 12:00:09 -5.541057 0.4039 BFGS: 21 12:00:09 -5.550354 1.7077 BFGS: 22 12:00:09 -5.561118 0.3295 BFGS: 23 12:00:09 -5.571989 0.3297 BFGS: 24 12:00:09 -5.325982 1.6277 BFGS: 25 12:00:09 -5.590947 0.4720 BFGS: 26 12:00:09 -5.559236 0.3329 BFGS: 27 12:00:09 -5.581635 0.3157 BFGS: 28 12:00:09 -5.310092 2.1232 BFGS: 29 12:00:09 -5.582313 0.9937 BFGS: 30 12:00:09 -5.460432 0.7097 BFGS: 31 12:00:10 -5.551491 0.3550 BFGS: 32 12:00:10 -5.578869 1.1455 BFGS: 33 12:00:10 -5.568943 0.3095 BFGS: 34 12:00:10 -5.579210 0.3042 BFGS: 35 12:00:10 -5.317244 1.7866 BFGS: 36 12:00:10 -5.587033 0.7966 BFGS: 37 12:00:10 -5.461176 0.7112 BFGS: 38 12:00:10 -5.559699 0.3170 BFGS: 39 12:00:10 -5.592966 0.3663 BFGS: 40 12:00:10 -5.583208 0.2907 BFGS: 41 12:00:11 -5.592933 0.1862 BFGS: 42 12:00:11 -5.545587 1.7609 BFGS: 43 12:00:11 -5.594824 0.1668 BFGS: 44 12:00:11 -5.595295 0.1689 BFGS: 45 12:00:11 -5.595634 0.1508 BFGS: 46 12:00:11 -5.595848 0.1382 BFGS: 47 12:00:11 -5.596218 0.0995 BFGS: 48 12:00:11 -5.596359 0.0997 BFGS: 49 12:00:11 -5.596817 0.1094 BFGS: 50 12:00:11 -5.597326 0.1168 BFGS: 51 12:00:11 -5.598318 0.1175 BFGS: 52 12:00:12 -5.552859 0.4600 BFGS: 53 12:00:12 -5.574159 0.7846 BFGS: 54 12:00:12 -5.588147 0.5505 BFGS: 55 12:00:12 -5.595027 0.2559 BFGS: 56 12:00:12 -5.602665 0.1604 BFGS: 57 12:00:12 -5.604079 0.0749 BFGS: 58 12:00:12 -5.604216 0.1023 BFGS: 59 12:00:12 -5.604481 0.0826 BFGS: 60 12:00:12 -5.605446 0.0526 BFGS: 61 12:00:12 -5.606199 0.0744 BFGS: 62 12:00:13 -5.605357 0.4217 BFGS: 63 12:00:13 -5.607068 0.0346 BFGS: 64 12:00:13 -5.607500 0.0342 BFGS: 65 12:00:13 -5.581688 1.1910 BFGS: 66 12:00:13 -5.607893 0.0296 BFGS: 67 12:00:13 -5.608146 0.0301 BFGS: 68 12:00:13 -5.608311 0.1705 BFGS: 69 12:00:13 -5.608587 0.0554 BFGS: 70 12:00:13 -5.608691 0.0569 BFGS: 71 12:00:13 -5.609182 0.0947 BFGS: 72 12:00:13 -5.609566 0.0651 BFGS: 73 12:00:14 -5.600396 0.7815 BFGS: 74 12:00:14 -5.612574 0.1182 BFGS: 75 12:00:14 -5.614660 0.0937 BFGS: 76 12:00:14 -5.620260 0.0549 BFGS: 77 12:00:14 -5.619484 0.1918 BFGS: 78 12:00:14 -5.620855 0.0646 BFGS: 79 12:00:14 -5.620925 0.0391 BFGS: 80 12:00:14 -5.620993 0.0274 BFGS: 81 12:00:14 -5.621018 0.0179 BFGS: 82 12:00:14 -5.621036 0.0132 BFGS: 83 12:00:14 -5.621049 0.0236 BFGS: 84 12:00:15 -5.621061 0.0309 BFGS: 85 12:00:15 -5.621082 0.0386 BFGS: 86 12:00:15 -5.621136 0.0500 BFGS: 87 12:00:15 -5.621274 0.0678 BFGS: 88 12:00:15 -5.621747 0.0900 BFGS: 89 12:00:15 -5.626878 0.1020 BFGS: 90 12:00:15 -5.626784 0.2829 BFGS: 91 12:00:15 -5.630610 0.1543 BFGS: 92 12:00:15 -5.631596 0.0634 BFGS: 93 12:00:15 -5.631780 0.0422 BFGS: 94 12:00:16 -5.631856 0.0149 BFGS: 95 12:00:16 -5.631868 0.0015 BFGS: 96 12:00:16 -5.631869 0.0004 BFGS: 97 12:00:16 -5.631869 0.0001 Relaxing: 2it [00:41, 20.62s/it] Saved to df.
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The optimized structures and energies are included in the result file.
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[9]
!cat results.json
{"final_structure":{"MP_mp-1006815":{"@module":"pymatgen.core.structure","@class":"Structure","charge":0,"lattice":{"matrix":[[7.230147594,-0.6129705837,-0.2359834023],[-4.1459218943,7.1809473651,0.4719668046],[-0.4390057784,0.760380313,14.2020498528]],"pbc":[true,true,true],"a":7.2599211658,"b":8.3052649614,"c":14.2291645685,"alpha":83.2045861827,"beta":93.8807974416,"gamma":124.8894017167,"volume":698.8008411647},"properties":{},"sites":[{"species":[{"element":"Cs","occu":1}],"abc":[-0.0,-0.0,0.0],"xyz":[0.0,-0.0,0.0],"properties":{},"label":"Cs"},{"species":[{"element":"H","occu":1}],"abc":[-0.0,0.1209169629,0.7997949989],"xyz":[-0.8524269099,1.4764467177,11.4157972395],"properties":{},"label":"H"},{"species":[{"element":"H","occu":1}],"abc":[0.0,0.8790830371,0.2002050011],"xyz":[-3.7325007628,6.4648809604,3.258219418],"properties":{},"label":"H"}]},"MP_mp-1093779":{"@module":"pymatgen.core.structure","@class":"Structure","charge":0,"lattice":{"matrix":[[16.0670444689,-0.0,-0.0],[-0.0,14.2111,-0.0],[-0.0,-0.0,20.0251]],"pbc":[true,true,true],"a":16.0670444689,"b":14.2111,"c":20.0251,"alpha":90.0000000001,"beta":90.0,"gamma":90.0,"volume":4572.3386054619},"properties":{},"sites":[{"species":[{"element":"K","occu":1}],"abc":[0.0,0.0,0.0],"xyz":[0.0,0.0,0.0],"properties":{},"label":"K"},{"species":[{"element":"Na","occu":1}],"abc":[0.1905935253,0.5,0.5],"xyz":[3.0622746457,7.10555,10.01255],"properties":{},"label":"Na"},{"species":[{"element":"Na","occu":1}],"abc":[0.8094064747,0.5,0.5],"xyz":[13.0047698232,7.10555,10.01255],"properties":{},"label":"Na"},{"species":[{"element":"Tl","occu":1}],"abc":[0.0,0.5,0.5],"xyz":[0.0,7.10555,10.01255],"properties":{},"label":"Tl"}]}},"final_energy":{"MP_mp-1006815":-5.6318688293,"MP_mp-1093779":-3.5466640916}}代码
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The structures that pass the check are saved as CIF format files in the directory relaxed.
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[7]
!ls relaxed
final-CsH2.cif final-KNa2Tl.cif
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Optimize Structures by Submitting Workflow
First create a configuration file for workflow. After structures are optimized, their convergence (skip with "check_convergence": false in relax.inputs of the config) and duplicate in OpenLAM Database or not (skip with "check_duplicate": false relax.inputs of the config) will be checked by default. The latter check requires environmental variable BOHRIUM_ACCESS_KEY, which can be generated from https://bohrium.dp.tech/settings/user
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%env BOHRIUM_ACCESS_KEY=xxx
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[16]
%%file input.json
{
"relax": {
"inputs": {
"check_convergence": false
},
"image": "registry.dp.tech/dptech/prod-26832/lam-crystal-philately:dp-mptraj-ase-utype",
"executor": {
"image_pull_policy": "IfNotPresent",
"machine_dict": {
"batch_type": "Bohrium",
"context_type": "Bohrium",
"remote_profile": {
"input_data": {
"job_type": "container",
"platform": "ali",
"scass_type": "1 * NVIDIA T4_16g",
"maxRescheduleTimes": 10
}
}
},
"retry_on_submission_error": 10
}
}
}
Overwriting input.json
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Here, we allocate 2 parallel tasks to optimize the 2 example structures
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[9]
!mkdir -p part0 part1
!cp openlam/examples/data/MP_mp-1006815.cif part0
!cp openlam/examples/data/MP_mp-1093779.cif part1
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Set environmental variables for workflows
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[21]
%env DFLOW_HOST=https://workflows.deepmodeling.com
%env DFLOW_K8S_API_SERVER=https://workflows.deepmodeling.com
%env DFLOW_S3_REPO_KEY=oss-bohrium
%env DFLOW_S3_STORAGE_CLIENT=dflow.plugins.bohrium.TiefblueClient
%env BOHRIUM_USERNAME=xxx
%env BOHRIUM_PASSWORD=xxx
%env BOHRIUM_PROJECT_ID=xxx
env: DFLOW_HOST=https://workflows.deepmodeling.com env: DFLOW_K8S_API_SERVER=https://workflows.deepmodeling.com env: DFLOW_S3_REPO_KEY=oss-bohrium env: DFLOW_S3_STORAGE_CLIENT=dflow.plugins.bohrium.TiefblueClient env: BOHRIUM_USERNAME=xxx env: BOHRIUM_PASSWORD=xxx env: BOHRIUM_PROJECT_ID=xxx
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Submit workflow
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[18]
!lam-opt submit input.json -i part0 part1 -m dp.pth
Workflow has been submitted (ID: parallel-relax-ghxnh, UID: d391313a-d9e5-4947-ab11-da0b7c88b13c) Workflow link: https://workflows.deepmodeling.com/workflows/argo/parallel-relax-ghxnh
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Click on the workflow link to track the workflow status
After the workflow finishes, download results by
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[19]
!lam-opt download parallel-relax-ghxnh -o outputs
!tree outputs
100%|█████████████████████████████████████████████| 5/5 [00:00<00:00, 7.92it/s] 100%|█████████████████████████████████████████████| 5/5 [00:00<00:00, 7.87it/s] outputs ├── 0 │ ├── relaxed │ │ └── final-CsH2.cif │ └── results.json └── 1 ├── relaxed │ └── final-KNa2Tl.cif └── results.json 4 directories, 4 files
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Query crystal structures from OpenLAM Database
Set environmental variable BOHRIUM_ACCESS_KEY, which can be generated from https://bohrium.dp.tech/settings/user
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[6]
%env BOHRIUM_ACCESS_KEY=xxx
env: BOHRIUM_ACCESS_KEY=xxx
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Query crystal structures by Python API (The method query_by_page is deprecated! Use query_by_offset instead.)
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[4]
from lam_optimize import CrystalStructure
data = CrystalStructure.query_by_offset()
print(data)
{'nextStartId': 18, 'items': [<lam_optimize.db.CrystalStructure object at 0x7f2ed5cf8590>, <lam_optimize.db.CrystalStructure object at 0x7f2ed57e71d0>, <lam_optimize.db.CrystalStructure object at 0x7f2ed5bb7290>, <lam_optimize.db.CrystalStructure object at 0x7f2ed5696450>, <lam_optimize.db.CrystalStructure object at 0x7f2ed5247810>, <lam_optimize.db.CrystalStructure object at 0x7f2ed5628950>, <lam_optimize.db.CrystalStructure object at 0x7f2ed5c0f650>, <lam_optimize.db.CrystalStructure object at 0x7f2ed5ba12d0>, <lam_optimize.db.CrystalStructure object at 0x7f2ed6d6ca90>, <lam_optimize.db.CrystalStructure object at 0x7f2ed4b899d0>]}
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The method query_by_offset accepts following arguments as query conditions
formula: Optional[str] = None
min_energy: Optional[float] = None
max_energy: Optional[float] = None
min_submission_time: Optional[datetime.datetime] = None
max_submission_time: Optional[datetime.datetime] = None
offset: int = 0
limit: int = 10
returned data includes nextStartId (as offset for next query), and items which is a list of CrystalStructure objects, each object has following properties
class CrystalStructure:
formula: str
structure: pymatgen.core.Structure
energy: float
submission_time: datetime.datetime
The method query merging paged results is also provided
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[5]
structures = CrystalStructure.query(formula="Sr2YSbO6")
print(structures)
[<lam_optimize.db.CrystalStructure object at 0x7f2ed5661050>, <lam_optimize.db.CrystalStructure object at 0x7f2ed54a3910>, <lam_optimize.db.CrystalStructure object at 0x7f2ed4baf850>]
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which returns a list of CrystalStructure objects.
NOTE: Calling non-paging method without query condition will be extremely slow.
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Query hull from OpenLAM Database
Set environmental variable BOHRIUM_ACCESS_KEY, which can be generated from https://bohrium.dp.tech/settings/user
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%env BOHRIUM_ACCESS_KEY=xxx
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Query hull by composition from OpenLAM Database using Python API. If the hull of the composition has not been calculated in OpenLAM Database, this function will throw a RuntimeError.
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from lam_optimize.utils import query_hull_by_composition
hull = query_hull_by_composition(["K", "Na", "Tl"])
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You can calculate energy above hull using the hull
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from lam_optimize.utils import get_e_above_hull
from pymatgen.core import Structure
structure = Structure.from_file("examples/data/MP_mp-1093779.cif")
get_e_above_hull(structure, hull, 0.123)
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