{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5c1592f9-a581-42e9-8b71-cd4ac44181ec",
   "metadata": {},
   "source": [
    "# AI2-Cat Calculation Notebook\n",
    "此 Notebook 将演示如何从一个结构文件出发，借助 `ai2-cat` 所提供的工具构建执行 AIMD, 势函数训练和性质计算的配置文件并执行以得到所需求数据的方法。\n",
    "\n",
    "1. 提供初始结构，进行AIMD模拟获取获取轨迹文件与相关能量和力等信息 (可选)\n",
    "2. 收集轨迹中的结构与对应的能量和力转化为机器学习势函数可训练的数据集格式\n",
    "3. 基于上述的训练数据集训练机器学习势函数\n",
    "4. 基于上述的机器学习势函数进行分子动力学模拟+增强采样，获取新的结构\n",
    "5. 基于力误差判据筛选出新的结构\n",
    "6. 对上述的新结构进行第一性原理计算获取结构对应的能量和力并加入数据集\n",
    "7. 迭代步骤3-6直至步骤5无法筛选出新的构象\n",
    "8. 基于最后的机器学习势函数进行分子动力学模拟+增强采样\n",
    "9. 基于模拟结果计算反应自由能\n"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "id": "2273b802-7eee-48ac-a059-1975c7b52f5a",
   "metadata": {},
   "source": [
    "![5253d51c6dded94850b0f214716b1ca.jpg](attachment:4598c744-3184-49ae-8af4-6c0b0a75e9a5.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b6f664-e160-4ab0-91ca-6d935c2983ba",
   "metadata": {},
   "source": [
    "### 准备工作\n",
    "\n",
    "* [ ] 将此 Notebook 下载到电脑。\n",
    "* [ ] 在 Open Ondemand 打开的 Jupyter Lab 页面中，通过左边栏上传该文件到工作目录中\n",
    "* [ ] 打开该 Notebook\n",
    "* [ ] 将初始结构文件上传到 Notebook 中\n",
    "\n",
    "上述操作仅在初次或者需要更新 Notebook 时执行即可。\n",
    "\n",
    "完成 Notebook 上传后，即可按顺序执行各个模块。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caf610bd-d7bf-4628-b830-bca3b24de59b",
   "metadata": {
    "scene__Default Scene": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [],
   "source": [
    "# 每次新打开 notebook 或者重启 kernel 后，该模块均需要执行一次\n",
    "%matplotlib widget\n",
    "%gui asyncio\n",
    "\n",
    "from ai2_kit.feat import catalysis as ai2cat\n",
    "from ai2_kit.feat.catalysis import ui\n",
    "import os\n",
    "\n",
    "AI4EC_DIR              = '/public/groups/ai4ec/data'\n",
    "CP2K_DATA              = f'{AI4EC_DIR}/cp2k'\n",
    "CP2K_BATCH_TEMPLATE    = f'{AI4EC_DIR}/slurm/ai2cat-cp2k.sbatch'\n",
    "LAMMPS_BATCH_TEMPLATE  = f'{AI4EC_DIR}/slurm/ai2cat-lammps.sbatch'\n",
    "AI2KIT_EXECUTOR_CONFIG = f'{AI4EC_DIR}/ai2-kit/executor.yml'\n",
    "\n",
    "CP2K_DEFAULTS = dict(\n",
    "    basis_set_file = os.path.join(CP2K_DATA, 'BASIS_MOLOPT'),\n",
    "    potential_file = os.path.join(CP2K_DATA, 'GTH_POTENTIALS'),\n",
    "    parameter_file = os.path.join(CP2K_DATA, 'dftd3.dat'),\n",
    ")\n",
    "\n",
    "ui_helper = ui.get_the_ui_helper()\n",
    "print(\"Current Directory Is: \", os.getcwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96ca99a5-ab13-4dfc-b32a-7f0157adf9c3",
   "metadata": {},
   "source": [
    "### 查看初始结构（可选）\n",
    "初始结构上传后，可执行以下代码对结构进行可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3d1e96e-e55d-4928-ace0-52780f22f9ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ase.visualize import view\n",
    "from ase.io import read\n",
    "from IPython.display import Code, display, Markdown\n",
    "\n",
    "atoms = read('./POSCAR')  # 可更改此路径以改变要显示的文件\n",
    "v = view(atoms, viewer='ngl')\n",
    "v.view.add_ball_and_stick()\n",
    "display(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8636f5a9",
   "metadata": {},
   "source": [
    "## AIMD生成初始数据集 (可选)\n",
    "\n",
    "初始数据集是启动势函数训练的必要输入。如果已经有适用于 DeepMD 的初始数据集和用于 LAMMPS 结构搜索的初始结构，则可以直接上传相应数据直接进入下一步的势函数训练中。\n",
    "\n",
    "如果没有所需的数据，则可以采用本节所提供的 AIMD 相关功能生成所需的数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a10a9b-48f2-4ff9-a27f-ac97918dd716",
   "metadata": {},
   "source": [
    "### 生成 AIMD 配置\n",
    "计算所需要的结构文件（支持 xyz, cif, POSCAR 等格式) 需自行构建并上传，然后使用以下命令即可生成用于 AIMD 执行的 CP2K 配置。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6215c8f0-6f30-4f19-9710-200885af7402",
   "metadata": {},
   "outputs": [],
   "source": [
    "! mkdir -p ./00-aimd\n",
    "ui_helper.gen_aimd_config(out_dir='./00-aimd', **CP2K_DEFAULTS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5417448a-48ac-4eab-8ec1-b8b560d1ceb0",
   "metadata": {},
   "source": [
    "此时 `00-aimd` 目录里能找到生成的 CP2K 配置和数据文件。\n",
    "通过左边栏打开文件可进行检查或编辑。\n",
    "确认无误后可使用 CP2K 作业模板提交任务。\n",
    "\n",
    "### 提交 AIMD 作业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db955539-3729-41f5-9cea-325e562a8fb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 复制作业提交脚本到任务目录 (复制后可根据需要进行调整)\n",
    "! cp {CP2K_BATCH_TEMPLATE} ./00-aimd/cp2k.sbatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0dc5197-e6f0-4706-8cb5-d071af40c900",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提交作业\n",
    "! (cd ./00-aimd && sbatch cp2k.sbatch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef5eec22-71c6-4116-942d-ae47d5c96e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 检查任务队列\n",
    "! squeue -u $USER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "076c07fb-3ab7-4312-905f-4527631c5914",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 检查作业执行情况，该命令需要手动停止\n",
    "! tail -f ./00-aimd/output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91fd6859",
   "metadata": {},
   "source": [
    "### 生成 DeepMD 数据集和 LAMMPS 初始文件\n",
    "\n",
    "AIMD 作业完成后会在工作目录下生成相应的结构和力数据。通过 `ai2-kit` 所提供的数据格式处理工具可以轻松地将其转换成所需的数据格式。\n",
    "\n",
    "相关工具的详细使用文档，可见 [ai2-kit ase](https://github.com/chenggroup/ai2-kit/blob/main/doc/manual/ase.md) 和 [ai2-kit dpdata](https://github.com/chenggroup/ai2-kit/blob/main/doc/manual/dpdata.md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b590f7c0-e929-4dfe-b34d-b64d9cede0c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成初始训练数据集\n",
    "! ai2-kit tool dpdata read ./00-aimd --fmt cp2kdata/md --cp2k_output_name=output - write ./data/train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f163421c-706e-41af-b424-288a88bf4acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成初始结构\n",
    "# 以下命令以每 20 帧为间隔从 AIMD 轨迹中抽取结构\n",
    "# 并限制最大帧数为 20\n",
    "# 使用 `set_by_ref` 从原始结构文件获取 cell, pbc 等缺失信息\n",
    "# 然后以 xyz 格式写入指定位置\n",
    "# 实际使用时可根据实际需求对参数进行调整\n",
    "! ai2-kit tool ase read ./00-aimd/*-pos-1.xyz --index '::20' - limit 20 - set_by_ref POSCAR - write ./data/explore/init.xyz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c1cde86",
   "metadata": {},
   "source": [
    "## 训练势函数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e9afbb-d38f-4f35-9eb3-a5a1524bd54b",
   "metadata": {},
   "source": [
    "### 生成 ai2-kit 势函数训练工作流配置\n",
    "\n",
    "AIMD 作业顺利完成后可从其输出得到轨迹数据。这里可同样借助 `ai2-cat` 自动生成相关配置文件。需要生成的文件包括\n",
    "1. CP2K 配置文件 cp2k.inp\n",
    "2. DeepMD 配置文件 deepmd.json\n",
    "3. plumed 配置文件 plumed.inp\n",
    "4. ai2-kit cll-mpl-training 配置文件 training.yml 和 executor.yml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f4c9a1-1851-4832-bcf9-b0b21357a23b",
   "metadata": {},
   "source": [
    "配置生成分为两部分，首先执行下面代码生成工作流所依赖的第三方软件的配置，包括数据标注用的CP2K配置和增强采样需要用到的plumed配置，执行成功后将在输出目录内产生 cp2k.inp 和 plumed.inp 两个文件。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffa5c521-bb0e-4dfc-950b-ce4481d66e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "! mkdir -p ./01-train\n",
    "ui_helper.gen_train_vendors_config(out_dir='./01-train', **CP2K_DEFAULTS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4ddc17-bcd7-4b0c-918a-f74e0e2be9bb",
   "metadata": {},
   "source": [
    "接下来是生成工作流相关的配置。\n",
    "\n",
    "> 在工作流配置中使用到的DeepMD训练数据和LAMMPS需要首先注册为 artifacts, 再通过对应的 key 进行引用。\n",
    ">\n",
    "> 例如，通过上述命令产生的假设 DeepMD 训练数据位于 ./data/train/Ag13O2, 要使用这笔数据，首先要在配置界面中的 artifacts字段里添加一个新artifact, 其中key可以任意指定（例如 train-data), 并设定 url 为 ./data/train/Ag13O2。设置完成后，即可在 train data 的下拉中选择刚刚配置 train-data 引用该数据。\n",
    ">\n",
    "> 同理，LAMMPS 的初始结构也需要通过上述方法添加后使用，假设训练数据位于 ./data/explore/init.xyz，可添加一个key为 explore-data 的 artifact, 并指定 url 为 ./data/explore/init.xyz。除此之外，由于新搜索的结构需要被标记，因此需要额外选择要使用的 CP2K 文件，例如 ./01-train/cp2k.inp, 由于需要使用plumed进行增强采样，因此还需要选择相应的 plumed 配置，例如 ./01-train/plumed.inp\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccfd7bbc-76fc-43b7-8059-183d295c74d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = os.path.abspath('./01-train/work_dir')\n",
    "! mkdir -p  {train_dir}\n",
    "ui_helper.gen_train_config(out_dir='./01-train', steps=400000)\n",
    "! ai2-kit tool yaml load {AI2KIT_EXECUTOR_CONFIG} - set_value \"executors.ikkem.work_dir\" {train_dir} - dump > ./01-train/executor.yml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c1e93c7-9e5a-4b53-98bf-175d9b2d8392",
   "metadata": {},
   "source": [
    "### 编辑 `ai2-kit` 势函数训练工作流配置\n",
    "目前由于技术方案的限制，自动生成的配置文件中仍有部分需要人工进行调整，必要改动如下\n",
    "* `plumed.inp`, plumed 的配置, 需要由用户定义增强采样的反应坐标, 以及其它所需的命令\n",
    "  * 例如增加 `CV1: DISTANCE ATOMS=14,15`，根据具体的研究目标进行配置\n",
    "* `training.yml`，需要用户指定使用的数据路径，并在工作流中引用\n",
    "  * 例如在 `artifacts` 一节中需要配置所需使用的体系等\n",
    "\n",
    "更具体的配置文档可参考相关 [文档](https://github.com/chenggroup/ai2-kit/blob/main/doc/manual/cll-workflow.zh.md) 和 [配置示例](https://github.com/chenggroup/ai2-kit/tree/main/example/config/cll-mlp-training)。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77cc500c-f151-43b9-ac04-57d76614b2e4",
   "metadata": {},
   "source": [
    "### 执行 `ai2-kit` 势函数训练工作流\n",
    "\n",
    "配置确认无误后，即可使用以下命令在后台启动训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9b06d1f-7e04-48f8-8a8c-06fef22f4483",
   "metadata": {},
   "outputs": [],
   "source": [
    "project_prefix = 'run-01'  # 请根据项目的工作目录修改\n",
    "os.system(f\"(cd ./01-train && nohup ai2-kit workflow cll-mlp-training *.yml --executor ikkem --path-prefix {project_prefix} --checkpoint {project_prefix}.pickle &> nohup.out&)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd06350d-81c5-4a1e-8c02-e59b694d4819",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 查看日志输出以确定执行是否正常, 注意该命令需要手动退出\n",
    "! tail -f ./01-train/nohup.out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8c00880",
   "metadata": {},
   "source": [
    "### 阶段性结果检查\n",
    "在很多情况下，尤其是针对复杂体系，势函数的训练往往不是一蹴而就。因此，一种比较稳健的策略阶段性地执行工作流，即在训练开始时指定较小的 `max_iter`, 在训练完成后对输出进行检查，并根据结果对配置进行必要的调整 (手动打开配置文件进行更新) 再次执行相同的训练命令，此时工作流会继承先前的状态，继续执行后续的迭代，直到势函数的训练结果满足要求。\n",
    "\n",
    "以下使用 `ai2-cat` 工具包中提供的工具对结果进行检查。\n",
    "\n",
    "#### 检查 DeepMD 输出\n",
    "检查势函数训练的力误差与能量误差随训练步数的演化关系，误差的演化应为指数下降的趋势最后在一个相对较小的值附近震荡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bff02df7-1074-45ad-91a6-1b71ee98cd0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ui_helper.inspect_deepmd_output('./01-train/work_dir')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af36d621-0cc0-4124-887b-43c75842b0f4",
   "metadata": {},
   "source": [
    "#### 检查 LAMMPS 输出\n",
    "检查模拟采样过程中，模型最大力偏差随模拟时间的变化关系，在初期几轮采样过程中该模型偏差会较大属于正常现象，迭代到后期模型偏差应大部分落在精确区间内，检查反应坐标随时间的变化关系，当模拟时间较短，反应坐标变化不会太大应在某个特定值（如反应始态或者终态反应坐标所对应的值）附近震荡，当模拟时间较长，反应坐标会出现较大变化（应在反应始态和终态反应坐标所对应的值之间来回震荡）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "662a9a1e-0bfd-49bb-b27c-c57ba6283a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "ui_helper.inspect_lammps_output('./01-train/work_dir')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab179bb",
   "metadata": {},
   "source": [
    "## 基于势函数进行动态催化模拟\n",
    "\n",
    "在得到满足要求的势函数后，即可使用势函数进行动态催化模拟以得到相关性质，并对输出结果进行分析得到报告。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2fbe152-1a70-4830-b799-2e943b6a6727",
   "metadata": {},
   "source": [
    "### 配置生成\n",
    "\n",
    "生成模拟所需lammps以及plumed配置文件，依据反应坐标配置plumed输入文件\n",
    "* `plumed.dat`, plumed 的配置, 需要由用户定义增强采样的反应坐标, 以及其它所需的命令\n",
    "  * 例如增加 `CV1: DISTANCE ATOMS=14,15`，根据具体的研究目标进行配置\n",
    " \n",
    "以下命令可以在 生成基本的作业模板，模板生成后可进入 `./02-simluation` 目录对其进行检查和编辑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78da0415-b3f1-491c-abd1-b485b2fedede",
   "metadata": {},
   "outputs": [],
   "source": [
    "ui_helper.gen_lammps_config(out_dir='./02-simulation', work_dir='./01-train/work_dir/')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a815af7f-19ec-4075-aed2-ae04329d2474",
   "metadata": {},
   "source": [
    "### 提交作业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3c04427-abe8-4a7c-a8b7-e75a2a6287a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 复制作业提交脚本到工作目录\n",
    "! cp {LAMMPS_BATCH_TEMPLATE} ./02-simulation/lammps.sbatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b4544a8-d907-42da-93b7-c45f6122fe21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提交作业\n",
    "! (cd ./02-simulation/ && sbatch lammps.sbatch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71e89f68-68bf-4d04-9ffa-e6a49b59a5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 检查任务队列\n",
    "! squeue -u $USER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2a5019f-6890-4a98-97a3-9bafd83f540e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 检查实时日志，该命令需要手动停止\n",
    "! tail -f ./02-simulation/lammps.log"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298b04cc-61df-4511-b09a-1feae6566b36",
   "metadata": {},
   "source": [
    "### 生成报告\n",
    "上述计算完成后，会在目录下生成一个名为 `fes.dat` 的文件，通过以下代码可将其可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5480f06-6be5-4529-8234-69d3f723603f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fes_data_path = './02-simulation/fes.dat'\n",
    "\n",
    "cv_fes = np.loadtxt(fes_data_path)\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n",
    "\n",
    "ax.plot(cv_fes[:,0],cv_fes[:,1],lw=2)\n",
    "ax.set_xlabel(r'$CV$')\n",
    "ax.set_ylabel(r'$Free \\ energy \\ (kJ/mol)$')\n",
    "ax.set_title(r'$Free \\ energy \\ curve$');"
   ]
  }
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