100 lines
3.2 KiB
YAML
100 lines
3.2 KiB
YAML
# param.yaml
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project: "LYC_model1"
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# 1. 初始文件定义 (对应 data/ 目录)
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files:
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poscar: "model1.vasp"
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potcar: "POTCAR"
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initial_pot: "nep89.txt" # 第一轮 MD 用的势函数
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label: "Li Y Cl"
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# 2. 迭代流程控制
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iterations:
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# --- 第一轮 ---
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- id: 0
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steps:
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# Step 1: MD (预热 + 采样)
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# 逻辑：会把 nep.txt (来自 initial_pot) 和 model.xyz 准备好
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- name: "00.md"
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sub_tasks:
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# 你提到可能有预热，也可能有加工，这里支持串行执行
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- template_sub: "preheat" # 使用 template/00.md/preheat/run.in
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- template_sub: "production" # 使用 template/00.md/production/run.in
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executor: "gpumd" # 对应 machine.yaml
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# Step 2: 筛选
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- name: "01.select"
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method: "random"
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params: [90, 120]
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# Step 3: SCF (VASP)
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# 逻辑：cp template/02.scf/INCAR; check KPOINTS; cp data/POTCAR
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- name: "02.scf"
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executor: "vasp_std" # 对应 machine.yaml (可能调用 vasp_std.sh)
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# Step 4: 训练
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# 逻辑：cp template/03.train/nep.in
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- name: "03.train"
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executor: "nep_local"
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- id: 1
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steps:
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# Step 1: MD (预热 + 采样)
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# 逻辑：会把 nep.txt (来自 initial_pot) 和 model.xyz 准备好
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- name: "00.md"
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sub_tasks:
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# 你提到可能有预热，也可能有加工，这里支持串行执行
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- template_sub: "preheat" # 使用 template/00.md/preheat/run.in
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- template_sub: "production" # 使用 template/00.md/production/run.in
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executor: "gpumd" # 对应 machine.yaml
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# Step 2: 筛选
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- name: "01.select"
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method: "distance"
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params: [90, 120]
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# Step 3: SCF (VASP)
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# 逻辑：cp template/02.scf/INCAR; check KPOINTS; cp data/POTCAR
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- name: "02.scf"
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executor: "vasp_std" # 对应 machine.yaml (可能调用 vasp_std.sh)
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# Step 4: 训练
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# 逻辑：cp template/03.train/nep.in
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- name: "03.train"
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executor: "nep_local"
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# --- 第二轮 ---
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- id: 2
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steps:
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- name: "00.md"
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sub_tasks:
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- template_sub: "preheat"
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- template_sub: "production" # 第二轮可能只需要 sampling
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# 注意：这一轮的 nep.txt 会自动指向 iter_00/03.train/nep.txt
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- name: "01.select"
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method: "distance"
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params: [0.01, 60, 90]
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- name: "02.scf"
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executor: "vasp_std"
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- name: "03.train"
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executor: "nep_local"
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- name: "04.predict"
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# [新增] 自定义模型文件 (位于 data/ 目录下)，不填则使用当前训练结果
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# custom_nep: "nep_final_best.txt"
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# [新增] 自定义预测结构 (位于 data/ 目录下)，不填则使用 00.md 的结果
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# 注意：这里填写 .vasp 文件，程序会自动转化为 model.xyz
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custom_poscar: "model1_supercell.vasp"
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conditions:
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- {T: 375, time: "15ns"}
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- { T: 400, time: "5ns" }
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- { T: 425, time: "2ns" }
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- { T: 450, time: "1ns" }
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- { T: 500, time: "1ns" }
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- { T: 600, time: "1ns" }
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- { T: 700, time: "1ns" }
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- { T: 800, time: "1ns" }
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- { T: 900, time: "1ns" } |