# param.yaml

project: "LGPS"

# 1. 初始文件定义 (对应 data/ 目录)
files:
  poscar: "LGPS.vasp"
  potcar: "POTCAR"
  initial_pot: "nep89.txt" # 第一轮 MD 用的势函数
  label: "Li Ge P S"

# 2. 迭代流程控制
iterations:
  # --- 第一轮 ---
  - id: 0
    steps:
      # Step 1: MD (预热 + 采样)
      # 逻辑：会把 nep.txt (来自 initial_pot) 和 model.xyz 准备好
      - name: "00.md"
        sub_tasks:
          # 你提到可能有预热，也可能有加工，这里支持串行执行
          - template_sub: "preheat"     # 使用 template/00.md/preheat/run.in
          - template_sub: "production"  # 使用 template/00.md/production/run.in
        executor: "gpumd" # 对应 machine.yaml

      # Step 2: 筛选
      - name: "01.select"
        method: "distance"
        params: [0.012, 60, 120]

      # Step 3: SCF (VASP)
      # 逻辑：cp template/02.scf/INCAR; check KPOINTS; cp data/POTCAR
      - name: "02.scf"
        executor: "vasp_std" # 对应 machine.yaml (可能调用 vasp_std.sh)

      # Step 4: 训练
      # 逻辑：cp template/03.train/nep.in
      - name: "03.train"
        executor: "nep_local"

  # --- 第二轮 ---
  - id: 1
    steps:
      - name: "00.md"
        sub_tasks:
          - template_sub: "preheat"
          - template_sub: "production" # 第二轮可能只需要 sampling
        # 注意：这一轮的 nep.txt 会自动指向 iter_00/03.train/nep.txt

      - name: "01.select"
        method: "distance"
        params: [0.01, 60, 120]

      - name: "02.scf"
        executor: "vasp_std"

      - name: "03.train"
        executor: "nep_local"
      - name: "04.predict"
        # 定义温度和时间列表
        conditions:
          - {T: 700, time: "1ns"}
          - {T: 800, time: "1ns"} # 支持不同温度不同时长
          - {T: 900, time: "1ns"}