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fix: make CH4 Lebesgue example self-contained
Replace one-line path stubs with usable JSON, POSCAR, INCAR, and documentation content. Add regression coverage that normalizes each JSON file and parses the bundled VASP inputs. Fixes #1915 Coding-Agent: Codex Codex-Version: codex-cli 0.149.0 Model: gpt-5.6-sol Reasoning-Effort: xhigh
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‎examples/CH4-lebesgue/CH4.POSCAR‎

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../init/CH4.POSCAR
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POSCAR file written by OVITO
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1.0000000000000000
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10.0000000000000000 0.0000000000000000 0.0000000000000000
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0.0000000000000000 10.0000000000000000 0.0000000000000000
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0.0000000000000000 0.0000000000000000 10.0000000000000000
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H C
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4 1
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Direct
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0.5381543385532134 0.4068608007477559 0.3605730142093164
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0.3945396557300829 0.4803205689390709 0.4384688400135213
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0.5520924283424902 0.5654502852627673 0.4427087377775140
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0.5281853047372259 0.4164147551778533 0.5391826568442692
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0.5032505926369848 0.4672551598725561 0.4452323411553799
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0.00000000E+00 0.00000000E+00 0.00000000E+00
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0.00000000E+00 0.00000000E+00 0.00000000E+00
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0.00000000E+00 0.00000000E+00 0.00000000E+00
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0.00000000E+00 0.00000000E+00 0.00000000E+00
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0.00000000E+00 0.00000000E+00 0.00000000E+00

‎examples/CH4-lebesgue/INCAR_methane‎

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../run/dp1.x-lammps-vasp/CH4/INCAR_methane
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PREC=A
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ENCUT=400
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ISYM=0
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ALGO=Fast
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EDIFF=1.000000e-06
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LREAL=False
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NPAR=4
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KPAR=1
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NELM=120
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NELMIN=4
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ISIF=2
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ISMEAR=0
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SIGMA=0.20000
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IBRION=-1
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NSW=0
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LWAVE=F
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LCHARG=F
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PSTRESS=0
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KSPACING=0.500000
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KGAMMA=.FALSE.

‎examples/CH4-lebesgue/INCAR_methane.md‎

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../init/INCAR_methane.md
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PREC=A
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ENCUT=400
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ISYM=0
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ALGO=Fast
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EDIFF=1.000000e-06
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LREAL=False
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NPAR=4
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KPAR=1
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NELM=120
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NELMIN=4
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ISIF=2
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ISMEAR=0
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SIGMA=0.20000
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IBRION=0
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POTIM=0.5
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NSW=10
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LWAVE=F
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LCHARG=F
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PSTRESS=0
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KSPACING=0.500000
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KGAMMA=.FALSE.

‎examples/CH4-lebesgue/INCAR_methane.rlx‎

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../init/INCAR_methane.rlx
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PREC=A
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ENCUT=400
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ISYM=0
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ALGO=Fast
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EDIFF=1.000000e-06
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LREAL=False
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NPAR=4
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KPAR=1
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NELM=120
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NELMIN=4
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ISIF=2
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ISMEAR=0
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SIGMA=0.20000
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IBRION=2
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NSW=50
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LWAVE=F
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LCHARG=F
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PSTRESS=0
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KSPACING=0.500000
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KGAMMA=.FALSE.

‎examples/CH4-lebesgue/README.md‎

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../machine/DeePMD-kit-2.x/lebesgue_v2_machine_README.md
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# CH4 workflow on the Lebesgue platform
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This directory is a self-contained methane example for preparing initial data
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and running DP-GEN with DeePMD-kit, LAMMPS, VASP, and the Lebesgue platform.
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Before running the example:
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1. provide `POTCAR_H` and `POTCAR_C` in this directory;
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1. replace the Lebesgue account placeholders in `lebesgue_v2_machine.json`;
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1. verify the program, image, and machine-type settings available to your
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Lebesgue account.
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Generate the initial data with:
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```bash
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dpgen init_bulk init.json lebesgue_v2_machine.json
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```
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Then run the iterative workflow from the generated data directory with:
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```bash
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dpgen run param_CH4_deepmd-kit-2.0.1.json lebesgue_v2_machine.json
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```
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The machine configuration uses placeholder credentials and resource settings.
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Consult the current DPDispatcher Bohrium/Lebesgue documentation before
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submitting production jobs.

‎examples/CH4-lebesgue/init.json‎

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../init/ch4.json
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{
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"stages": [
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1,
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2,
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3,
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4
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],
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"cell_type": "diamond",
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"super_cell": [
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1,
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1,
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1
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],
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"elements": [
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"H",
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"C"
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],
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"from_poscar": true,
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"from_poscar_path": "./CH4.POSCAR",
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"potcars": [
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"./POTCAR_H",
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"./POTCAR_C"
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],
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"relax_incar": "./INCAR_methane.rlx",
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"md_incar": "./INCAR_methane.md",
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"scale": [
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1.0
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],
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"skip_relax": false,
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"pert_numb": 30,
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"md_nstep": 10,
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"pert_box": 0.03,
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"pert_atom": 0.01,
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"coll_ndata": 5000,
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"_comment1": "that's all"
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}

‎examples/CH4-lebesgue/lebesgue_v2_machine.json‎

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../machine/DeePMD-kit-2.x/lebesgue_v2_machine.json
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{
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"api_version": "1.0",
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"deepmd_version": "2.0.1",
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"train": {
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"command": "dp",
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"machine": {
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"batch_type": "DpCloudServer",
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"context_type": "DpCloudServerContext",
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"local_root": "./",
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"remote_profile": {
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"email": "your lebesgue register email",
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"password": "your lebesgue password",
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"program_id": 0,
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"input_data": {
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"api_version": 2,
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"job_type": "indicate",
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"log_file": "00*/train.log",
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"grouped": true,
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"job_name": "dpgen_train_job",
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"disk_size": 100,
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"scass_type": "c8_m60_1 * NVIDIA P100",
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"platform": "ali",
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"image_name": "deepmd-kit:2.0.1",
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"on_demand": 0
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}
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}
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},
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"resources": {
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"batch_type": "DpCloudServer",
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"number_node": 1,
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"cpu_per_node": 4,
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"gpu_per_node": 1,
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"queue_name": "GPU",
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"group_size": 1
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}
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},
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"model_devi": {
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"command": "lmp -i input.lammps -v restart 0",
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"machine": {
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"batch_type": "DpCloudServer",
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"context_type": "DpCloudServerContext",
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"local_root": "./",
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"remote_profile": {
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"email": "your lebesgue register email",
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"password": "your lebesgue password",
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"program_id": 0,
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"input_data": {
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"api_version": 2,
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"job_type": "indicate",
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"log_file": "*/model_devi.log",
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"grouped": true,
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"job_name": "dpgen_model_devi_job",
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"disk_size": 200,
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"scass_type": "c8_m60_1 * NVIDIA P100",
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"platform": "ali",
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"image_name": "deepmd-kit:2.0.1",
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"on_demand": 0
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}
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}
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},
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"resources": {
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"batch_type": "DpCloudServer",
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"number_node": 1,
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"cpu_per_node": 4,
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"gpu_per_node": 1,
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"queue_name": "GPU",
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"group_size": 5
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}
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},
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"fp": {
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"command": "mpirun -n 16 vasp_std",
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"machine": {
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"batch_type": "DpCloudServer",
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"context_type": "DpCloudServerContext",
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"local_root": "./",
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"remote_profile": {
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"email": "your lebesgue register email",
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"password": "your lebesgue password",
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"program_id": 0,
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"input_data": {
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"api_version": 2,
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"job_type": "indicate",
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"log_file": "task*/fp.log",
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"grouped": true,
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"job_name": "dpgen_fp_job",
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"disk_size": 100,
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"scass_type": "c8_m8_cpu",
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"platform": "ali",
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"image_name": "vasp:5.4.4",
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"on_demand": 0
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}
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}
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},
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"resources": {
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"batch_type": "DpCloudServer",
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"number_node": 1,
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"cpu_per_node": 32,
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"gpu_per_node": 0,
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"queue_name": "CPU",
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"group_size": 5,
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"source_list": [
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"/opt/intel/oneapi/setvars.sh"
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]
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}
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}
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}

‎examples/CH4-lebesgue/param_CH4_deepmd-kit-2.0.1.json‎

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../run/dp2.x-lammps-vasp/param_CH4_deepmd-kit-2.0.1.json
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{
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"type_map": [
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"H",
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"C"
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],
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"mass_map": [
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1,
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12
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],
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"init_data_prefix": "./",
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"init_data_sys": [
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"CH4.POSCAR.01x01x01/02.md/sys-0004-0001/deepmd"
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],
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"sys_configs_prefix": "./",
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"sys_configs": [
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[
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"CH4.POSCAR.01x01x01/01.scale_pert/sys-0004-0001/scale*/00000*/POSCAR"
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],
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[
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"CH4.POSCAR.01x01x01/01.scale_pert/sys-0004-0001/scale*/00001*/POSCAR"
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]
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],
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"_comment1": " that's all ",
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"numb_models": 4,
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"default_training_param": {
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"model": {
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"type_map": [
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"H",
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"C"
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],
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"descriptor": {
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"type": "se_a",
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"sel": [
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16,
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4
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],
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"rcut_smth": 0.5,
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"rcut": 5.0,
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"neuron": [
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120,
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120,
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120
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],
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"resnet_dt": true,
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"axis_neuron": 12,
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"seed": 1
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},
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"fitting_net": {
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"neuron": [
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25,
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50,
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100
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],
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"resnet_dt": false,
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"seed": 1
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}
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},
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"learning_rate": {
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"type": "exp",
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"start_lr": 0.001,
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"decay_steps": 100
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},
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"loss": {
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"start_pref_e": 0.02,
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"limit_pref_e": 2,
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"start_pref_f": 1000,
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"limit_pref_f": 1,
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"start_pref_v": 0.0,
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"limit_pref_v": 0.0
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},
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"training": {
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"numb_steps": 2000,
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"batch_size": 1,
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"disp_file": "lcurve.out",
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"disp_freq": 1000,
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"save_freq": 1000,
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"save_ckpt": "model.ckpt",
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"disp_training": true,
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"time_training": true,
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"profiling": false,
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"profiling_file": "timeline.json",
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"_comment2": "that's all"
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}
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},
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"model_devi_dt": 0.002,
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"model_devi_skip": 0,
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"model_devi_f_trust_lo": 0.05,
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"model_devi_f_trust_hi": 0.15,
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"model_devi_clean_traj": true,
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"model_devi_jobs": [
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{
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"sys_idx": [
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0
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],
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"temps": [
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100
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],
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"press": [
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1.0
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],
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"trj_freq": 10,
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"nsteps": 300,
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"ensemble": "nvt",
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"_idx": "00"
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},
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{
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"sys_idx": [
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1
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],
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"temps": [
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100
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],
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"press": [
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1.0
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],
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"trj_freq": 10,
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"nsteps": 3000,
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"ensemble": "nvt",
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"_idx": "01"
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}
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],
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"fp_style": "vasp",
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"shuffle_poscar": false,
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"fp_task_max": 20,
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"fp_task_min": 5,
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"fp_pp_path": "./",
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"fp_pp_files": [
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"POTCAR_H",
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"POTCAR_C"
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],
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"fp_incar": "./INCAR_methane"
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}

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