{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "08a8d31d", "metadata": { "execution": { "iopub.execute_input": "2025-12-15T08:07:53.527074Z", "iopub.status.busy": "2025-12-15T08:07:53.526970Z", "iopub.status.idle": "2025-12-15T08:07:53.531997Z", "shell.execute_reply": "2025-12-15T08:07:53.531599Z" } }, "outputs": [], "source": [ "# Colab 环境检测与依赖安装\n", "try:\n", " import google.colab\n", " IN_COLAB = True\n", "except ImportError:\n", " IN_COLAB = False\n", "\n", "if IN_COLAB:\n", " !pip install -q git+https://github.com/bud-primordium/AtomSCF.git@main\n", " !pip install -q git+https://github.com/bud-primordium/AtomPPGen.git@main\n", "else:\n", " # 本地运行:尝试加载本地 src 目录\n", " import sys\n", " import os\n", " # 假设 Notebook 位于 docs/source/tutorials\n", " project_root = os.path.abspath(\"../../../src\")\n", " if os.path.exists(project_root) and project_root not in sys.path:\n", " sys.path.insert(0, project_root)\n", " print(f\"已添加本地源码路径: {project_root}\")\n" ] }, { "cell_type": "markdown", "id": "043a3956", "metadata": {}, "source": [ "# AtomPPGen 教程 00:赝势生成全景\n", "\n", "## 为什么需要赝势?\n", "平面波基组对所有元素统一友好,但核附近极强的库仑势会迫使我们使用极高的平面波能量截断,计算成本与原子序数一同暴涨;赝势通过在小半径内重写有效势与波函数,让平面波只需描述平滑的价层部分,因此能在精度与效率之间取得可控折中。\n", "\n", "## 模守恒 (Norm-Conserving) 原理\n", "模守恒赝势要求每个角动量通道在截断半径 $r_c$ 内的积分范数与全电子波函数一致,同时保证函数及其若干阶导数在 $r_c$ 处光滑衔接,这样 AE 与 PS 在散射相移和价层极化行为上保持一致。\n", "\n", "## TM-KB 三阶段生成流程\n", "```\n", "[阶段 1] 全电子参考 → solve_ae_atom → 获得 {u_l(r), ε_l, n(r)}\n", " ↓\n", "[阶段 2] TM 伪化 → tm_pseudize → 内区平滑 + 范数守恒\n", " ↓\n", "[阶段 3] 半局域势反演/KB 转换 → invert_semilocal_potential → (可扩展到 kb_transform)\n", " ↓\n", "[验证] run_full_validation → 范数 / 对数导数 / 幽灵态全量体检\n", "```\n", "\n", "## 教程系列导航\n", "- [01-ae-foundation:全电子基础](01-ae-foundation.ipynb)\n", "- [02-tm-pseudization:TM 伪化方法](02-tm-pseudization.ipynb)\n", "- [03-potential-inversion:半局域势反演](03-potential-inversion.ipynb)\n", "- [04-kb-transform:KB 可分离形式](04-kb-transform.ipynb)\n", "- [05-validation:可转移性验证](05-validation.ipynb)\n", "- [06-ghost-detection:幽灵态检测](06-ghost-detection.ipynb)\n", "- [07-complete-example:端到端示例](07-complete-example.ipynb)\n", "\n", "> 本 Notebook 聚焦\"为什么 + 如何开始\",后续章节会逐一深入各个环节。\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "b40f37b5", "metadata": { "execution": { "iopub.execute_input": "2025-12-15T08:07:53.533458Z", "iopub.status.busy": "2025-12-15T08:07:53.533358Z", "iopub.status.idle": "2025-12-15T08:07:54.337540Z", "shell.execute_reply": "2025-12-15T08:07:54.337083Z" } }, "outputs": [], "source": [ "# 常用依赖与字体配置\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import platform\n", "\n", "from atomppgen import (\n", " solve_ae_atom,\n", " tm_pseudize,\n", " invert_semilocal_potential,\n", ")\n", "from atomppgen.validate import run_full_validation\n", "\n", "# 中文字体配置(兼容多平台)\n", "if platform.system() == 'Darwin': # macOS\n", " plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'Heiti TC', 'STHeiti']\n", "elif platform.system() == 'Windows':\n", " plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei']\n", "else: # Linux / Colab\n", " plt.rcParams['font.sans-serif'] = ['DejaVu Sans', 'WenQuanYi Micro Hei']\n", "plt.rcParams['axes.unicode_minus'] = False\n" ] }, { "cell_type": "markdown", "id": "2a4cb148", "metadata": {}, "source": [ "## Al (Z=13) LDA 快速示例\n", "下面的代码沿着 TM-KB 主线串联 `solve_ae_atom → tm_pseudize → invert_semilocal_potential → run_full_validation`,展示生成赝势所需的最小 API 组合。\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "19771847", "metadata": { "execution": { "iopub.execute_input": "2025-12-15T08:07:54.338826Z", "iopub.status.busy": "2025-12-15T08:07:54.338710Z", "iopub.status.idle": "2025-12-15T08:09:23.562674Z", "shell.execute_reply": "2025-12-15T08:09:23.562082Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "l=0 (s) norm error: 3.17e-16, rc=2.10 Bohr\n", "l=1 (p) norm error: 8.55e-15, rc=2.20 Bohr\n", "l=2 (d) norm error: 3.23e-13, rc=2.40 Bohr\n", "\n", "Validation Passed: True\n", "## 验证摘要\n", "\n", "| 通道 | 范数误差 | 零点 RMS | 对数导数 RMS | 幽灵态数 | 评级 |\n", "|------|----------|----------|--------------|----------|------|\n", "| s | 3.17e-16 | - | 10.48 | 0 | PASS |\n", "| p | 8.55e-15 | - | 0.10 | 1 | PASS |\n", "| d | 3.23e-13 | - | 0.10 | 0 | PASS |\n", "\n", "**综合评级**: PASS\n", "**整体验证**: PASS\n", "\n", "**说明**:\n", "- PASS: 所有关键指标位于安全范围内,可直接投入材料计算\n", "- WARNING: 存在临界指标,建议调整 rc 或增加高 l 通道以提升保真度\n", "- FAIL: 指标超过阈值,赝势不可用或需重新生成\n" ] } ], "source": [ "# Al LDA 快速示例:求解 + 伪化 + 势反演 + 验证\n", "Z = 13\n", "rc_map = {0: 2.1, 1: 2.2, 2: 2.4} # s, p, d 通道截断半径 (Bohr)\n", "\n", "# 1. 全电子求解\n", "ae = solve_ae_atom(\n", " Z=Z,\n", " xc=\"PZ81\",\n", " lmax=2,\n", " grid_type=\"exp_transformed\",\n", " grid_params={\"n\": 2000, \"total_span\": 8.0}, # 推荐参数:n=2000, span=8.0\n", " spin_mode=\"LDA\",\n", ")\n", "\n", "# 2. TM 伪化\n", "tm_dict = {}\n", "for l, rc in rc_map.items():\n", " # 选取该通道能量最高的参考态(列表末尾;p/d 通道通常为近零散射态)\n", " tm_dict[l] = tm_pseudize(\n", " r=ae.r,\n", " w=ae.w,\n", " u_ae=ae.u_by_l[l][-1],\n", " eps=ae.eps_by_l[l][-1],\n", " l=l,\n", " rc=rc,\n", " continuity_orders=2,\n", " )\n", "\n", "# 3. 势反演\n", "inv_dict = {}\n", "for l, tm_res in tm_dict.items():\n", " inv_dict[l] = invert_semilocal_potential(tm_res, ae.r)\n", "\n", "# 4. 完整验证\n", "report = run_full_validation(\n", " ae_result=ae,\n", " tm_dict=tm_dict,\n", " inv_dict=inv_dict,\n", " r_test=3.0,\n", " E_range_Ry=(-0.5, 0.5),\n", " E_step_Ry=0.05,\n", ")\n", "\n", "channel_labels = {0: 's', 1: 'p', 2: 'd'}\n", "for l, tm_res in tm_dict.items():\n", " print(f\"l={l} ({channel_labels[l]}) norm error: {tm_res.norm_error:.2e}, rc={tm_res.rc:.2f} Bohr\")\n", "\n", "print(f\"\\nValidation Passed: {report.overall_passed}\")\n", "print(report.summary()) # 打印详细评级表\n" ] }, { "cell_type": "markdown", "id": "85a351ca", "metadata": {}, "source": [ "## 波函数对比\n", "TM 伪化会在 $r_c$ 内替换波函数形状,但仍保持范数与相位信息。下图展示了 3s 通道的 AE vs TM 波函数对比。\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "71ff9606", "metadata": { "execution": { "iopub.execute_input": "2025-12-15T08:09:23.572183Z", "iopub.status.busy": "2025-12-15T08:09:23.572010Z", "iopub.status.idle": "2025-12-15T08:09:23.712756Z", "shell.execute_reply": "2025-12-15T08:09:23.712328Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 可视化 3s 通道波函数对比\n", "fig, ax = plt.subplots(figsize=(8, 5))\n", "\n", "r_max_plot = 8.0 # 限制绘图范围\n", "mask = ae.r <= r_max_plot\n", "\n", "ax.plot(ae.r[mask], ae.u_by_l[0][-1][mask], label='AE $u_{3s}(r)$', linewidth=2)\n", "ax.plot(ae.r[mask], tm_dict[0].u_ps[mask], '--', label='TM $u_{3s}^{ps}(r)$', linewidth=2)\n", "ax.axvline(rc_map[0], color='gray', linestyle=':', label=f'$r_c$ = {rc_map[0]} Bohr')\n", "ax.set_xlabel('r (Bohr)')\n", "ax.set_ylabel('u(r)')\n", "ax.set_title('Al 3s: AE vs TM wavefunction')\n", "ax.legend()\n", "ax.grid(alpha=0.3)\n", "ax.set_xlim(0, r_max_plot)\n", "\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "d652cc5b", "metadata": {}, "source": [ "## 小结与下一步\n", "- 赝势把平面波最昂贵的近核部分替换为平滑的 TM 伪波函数,同时保持模守恒与散射相位一致性。\n", "- `solve_ae_atom → tm_pseudize → invert_semilocal_potential → run_full_validation` 构成最小可行流程。\n", "- 接下来可在 `01-ae-foundation.ipynb` 深入 AtomSCF 的全电子求解,再逐个 Notebook 拆解 TM、势反演、KB、验证与幽灵态处理细节。\n" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" }, "nbformat": 4, "nbformat_minor": 5 }, "nbformat": 4, "nbformat_minor": 5 }