gsj+LiYCl data

MSD/main.py

@@ -9,10 +9,11 @@ if __name__ == '__main__':
     # 调用新函数
     # 它会自动从 log.lammps 读取温度和体积
     results = calculate_conductivity_from_msd(
-        msd_file_path='data/msd_li.dat',
-        log_file_path='data/log.lammps',
+        msd_file_path='data/700K/msd_li.dat',
+        log_file_path='data/700K/log.lammps',
         ion_name='Li⁺',
         charge=1,
         num_ions=num_li_ions,
-        fit_fraction=0.5  # 可以根据图像调整此值
+        fit_start_fraction=0.0,
+        fit_end_fraction=1.0
     )

MSD/utils/MSD.py

@@ -63,7 +63,8 @@ def calculate_conductivity_from_msd(
         ion_name,
         charge,
         num_ions,
-        fit_fraction=0.5
+        fit_start_fraction=0.5,
+        fit_end_fraction=0.5
 ):
     """
     从MSD数据计算电导率 (v2)。
@@ -102,9 +103,10 @@ def calculate_conductivity_from_msd(
     time_ps = timesteps * timestep_ps
 
     # --- 3. 线性拟合计算扩散系数 ---
-    fit_start_index = int(len(time_ps) * (1 - fit_fraction))
-    fit_time_ps = time_ps[fit_start_index:]
-    fit_msd_values = msd_values[fit_start_index:]
+    fit_start_index = int(len(time_ps) * fit_start_fraction)
+    fit_end_index = int(len(time_ps) * fit_end_fraction)
+    fit_time_ps = time_ps[fit_start_index:fit_end_index]
+    fit_msd_values = msd_values[fit_start_index:fit_end_index]
 
     if len(fit_time_ps) < 2:
         print("错误: 用于拟合的数据点不足 (少于2个)，无法进行线性回归。")

MSD/utils/con2.py

@@ -0,0 +1,289 @@
+import os
+import re
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import stats
+
+# 物理常数（SI单位）
+kB = 1.380649e-23  # 玻尔兹曼常数 (J/K)
+q = 1.602176634e-19  # 元电荷 (C，Li+的电荷)
+ps_to_s = 1e-12  # 皮秒转秒
+angstrom2_to_m2 = 1e-20  # 埃²转米²
+angstrom3_to_m3 = 1e-30  # 埃³转米³
+
+
+def extract_temperature(folder_name):
+    """从文件夹名（如320k）提取温度（K）"""
+    match = re.search(r'(\d+)K', folder_name)
+    if match:
+        return int(match.group(1))
+    return None
+
+
+def read_msd(msd_path):
+    """读取msd_li.dat，返回时间（ps）和总MSD（Ų）"""
+    try:
+        data = np.loadtxt(msd_path, comments='#')
+    except Exception as e:
+        raise ValueError(f"读取MSD文件失败：{e}")
+    
+    if data.size == 0:
+        raise ValueError("MSD文件内容为空")
+    
+    # 时间步转实际时间（ps）：TimeStep × 0.001
+    time = data[:, 0] * 0.001  # 第一列是TimeStep，乘以0.001转为ps
+    msd_total = data[:, -1]    # 最后一列是总MSD
+    
+    # 打印时间范围（关键调试信息）
+    print(f"  MSD时间范围：{time.min():.1f} ~ {time.max():.1f} ps")
+    return time, msd_total
+
+
+def calculate_diffusion(time, msd, t_start, t_end):
+    """根据MSD计算扩散系数D（单位：m²/s）"""
+    # 筛选时间范围内的数据
+    mask = (time >= t_start) & (time <= t_end)
+    t_filtered = time[mask] * ps_to_s  # 转为秒
+    msd_filtered = msd[mask] * angstrom2_to_m2  # 转为m²
+    
+    # 检查筛选后的数据是否为空
+    if len(t_filtered) < 2:
+        raise ValueError(f"筛选后的数据点不足（仅{len(t_filtered)}个），请调整t_start/t_end")
+    
+    # 线性拟合：MSD vs 时间，斜率 = 6D
+    slope, _, r_value, _, _ = stats.linregress(t_filtered, msd_filtered)
+    D = slope / 6
+    
+    # 打印拟合信息（调试用）
+    print(f"  MSD拟合：斜率 = {slope:.6e}，R² = {r_value**2:.4f}")
+    return D
+
+
+def parse_lammps_data(data_path):
+    """解析LAMMPS data文件，提取盒子体积和Li+数量（Li的原子类型为1）"""
+    with open(data_path, 'r') as f:
+        lines = f.readlines()
+    
+    # 提取原子总数
+    atom_count_line = next((line for line in lines if 'atoms' in line), None)
+    if not atom_count_line:
+        raise ValueError("无法在data文件中找到原子总数")
+    total_atoms = int(atom_count_line.split()[0])
+    
+    # 提取盒子尺寸
+    x_line = next((line for line in lines if 'xlo xhi' in line), None)
+    y_line = next((line for line in lines if 'ylo yhi' in line), None)
+    z_line = next((line for line in lines if 'zlo zhi' in line), None)
+    
+    if not (x_line and y_line and z_line):
+        raise ValueError("无法在data文件中找到盒子尺寸")
+    
+    xlo, xhi = map(float, x_line.split()[:2])
+    ylo, yhi = map(float, y_line.split()[:2])
+    zlo, zhi = map(float, z_line.split()[:2])
+    
+    # 计算盒子体积（ų）
+    volume_angstrom3 = (xhi - xlo) * (yhi - ylo) * (zhi - zlo)
+    
+    # 手动指定Li的原子类型为1
+    li_type = 1
+    
+    # 统计Li原子数量
+    atoms_section_start = next((i for i, line in enumerate(lines) if 'Atoms' in line), None)
+    if atoms_section_start is None:
+        raise ValueError("无法在data文件中找到Atoms部分")
+    
+    li_count = 0
+    for i in range(atoms_section_start + 1, atoms_section_start + total_atoms + 1):
+        if i >= len(lines):
+            break
+        parts = lines[i].split()
+        if len(parts) >= 2 and int(parts[1]) == li_type:
+            li_count += 1
+    
+    if li_count == 0:
+        raise ValueError(f"未找到原子类型为{li_type}的Li原子")
+    
+    return li_count, volume_angstrom3
+
+
+def calculate_conductivity(D, T, n):
+    """根据Nernst-Einstein方程计算离子电导σ（单位：S/m）"""
+    sigma = (n * q**2 * D) / (kB * T)
+    return sigma
+
+def arrhenius_plot(temperatures, D_list, sigma_list):
+    """绘制阿伦尼乌斯图，拟合400K前后两条直线，基于320-400K数据外推300K电导"""
+    plt.rcParams["axes.unicode_minus"] = False  # 确保负号正常显示
+    temps = np.array(temperatures, dtype=float)
+    sigmas = np.array(sigma_list, dtype=float)
+    inv_T = 1000 / temps  # 1000/T (1/K)
+
+    # 数据分组：320-400K（用于拟合和外推）和>400K（仅拟合）
+    mask_below = (temps >= 320) & (temps <= 400)  # 320-400K区间
+    mask_above = temps > 400                     # 400K以上区间
+
+    # 检查低温度区间数据量
+    if np.sum(mask_below) < 2:
+        raise ValueError(f"320-400K区间数据点不足（仅{np.sum(mask_below)}个），无法拟合直线")
+
+    # 1. 拟合320-400K区间（用于外推300K）
+    inv_T_below = inv_T[mask_below]
+    ln_sigma_below = np.log(sigmas[mask_below])
+    slope_below, intercept_below, r_below, _, _ = stats.linregress(inv_T_below, ln_sigma_below)
+    Ea_below = -slope_below * kB * 1000  # 活化能（J）
+    Ea_below_eV = Ea_below / q           # 转换为eV
+
+    # 2. 拟合400K以上区间（若有足够数据）
+    slope_above = None
+    intercept_above = None
+    Ea_above_eV = None
+    r_above = None
+    if np.sum(mask_above) >= 2:
+        inv_T_above = inv_T[mask_above]
+        ln_sigma_above = np.log(sigmas[mask_above])
+        slope_above, intercept_above, r_above, _, _ = stats.linregress(inv_T_above, ln_sigma_above)
+        Ea_above = -slope_above * kB * 1000
+        Ea_above_eV = Ea_above / q
+
+    # 3. 外推300K的离子电导（基于320-400K拟合线）
+    T_target = 300  # 目标温度300K
+    inv_T_target = 1000 / T_target  # 1000/300 (1/K)
+    ln_sigma_target = slope_below * inv_T_target + intercept_below  # 用低温度区间拟合线外推
+    sigma_target = np.exp(ln_sigma_target)  # 转换为电导值
+
+    # 4. 绘图
+    fig, ax1 = plt.subplots(figsize=(10, 6))
+
+    # 绘制数据点
+    ax1.scatter(inv_T_below, ln_sigma_below, label='320-400K Data', color='blue', s=60, zorder=3)
+    if np.sum(mask_above) >= 1:
+        ax1.scatter(inv_T[mask_above], np.log(sigmas[mask_above]), label='>400K Data', color='red', s=60, zorder=3)
+
+    # 绘制拟合线（低温度区间）
+    x_fit_below = np.linspace(min(inv_T_below), max(inv_T_below), 100)
+    y_fit_below = slope_below * x_fit_below + intercept_below
+    ax1.plot(x_fit_below, y_fit_below, '--', color='blue', 
+             label=f'320-400K Fit (Eₐ={Ea_below_eV:.3f} eV, R²={r_below**2:.3f})', zorder=2)
+
+    # 绘制拟合线（高温度区间，若有）
+    if slope_above is not None:
+        x_fit_above = np.linspace(min(inv_T[mask_above]), max(inv_T[mask_above]), 100)
+        y_fit_above = slope_above * x_fit_above + intercept_above
+        ax1.plot(x_fit_above, y_fit_above, '--', color='red', 
+                 label=f'>400K Fit (Eₐ={Ea_above_eV:.3f} eV, R²={r_above**2:.3f})', zorder=2)
+
+    # 标记300K外推点
+    ax1.scatter([inv_T_target], [ln_sigma_target], color='green', marker='*', s=150, 
+                label=f'300K Extrapolated', zorder=4)
+
+    # 坐标轴设置
+    ax1.set_xlabel('1000/T (1/K)')
+    ax1.set_ylabel('ln(σ) (S/m)')
+    ax1.set_title('Arrhenius Plot with Dual Fitting')
+    ax1.legend()
+    ax1.grid(alpha=0.3)
+
+    # 顶部温度坐标轴
+    ax2 = ax1.twiny()
+    x1_min, x1_max = ax1.get_xlim()
+    ax2.set_xlim(x1_min, x1_max)
+    ax2.set_xticks(1000 / np.unique(temps))  # 显示现有温度刻度
+    ax2.set_xticklabels([f"{int(t)}" for t in np.unique(temps)])
+    ax2.set_xlabel('Temperature (K)')
+
+    plt.tight_layout()
+    plt.savefig('arrhenius_dual_fit.png', dpi=300)
+    plt.show()
+
+    # 输出结果
+    print("\n===== 拟合结果 =====")
+    print(f"320-400K区间：")
+    print(f"  活化能 Eₐ = {Ea_below_eV:.3f} eV (R² = {r_below**2:.3f})")
+    if Ea_above_eV is not None:
+        print(f">400K区间：")
+        print(f"  活化能 Eₐ = {Ea_above_eV:.3f} eV (R² = {r_above**2:.3f})")
+    
+    print(f"\n===== 300K外推结果（基于320-400K拟合） =====")
+    print(f"  离子电导率 σ = {sigma_target:.6e} S/m")
+    print(f"  转换单位：σ = {sigma_target * 10:.6e} mS/cm")  # 1 S/m = 0.1 mS/cm，乘以10转换
+
+
+if __name__ == "__main__":
+    # ------------ 必须根据实际数据调整的参数 ------------
+    base_dir = "../data"  # 主文件夹路径
+    t_start = 100  # MSD拟合起始时间（ps）
+    t_end = 2000    # MSD拟合结束时间（ps）
+    # ------------------------------------------------
+
+    temperatures = []
+    D_list = []
+    sigma_list = []
+    temp_folder_pattern = re.compile(r'^\d+K$')
+
+    for folder in os.listdir(base_dir):
+        folder_path = os.path.join(base_dir, folder)
+        if not os.path.isdir(folder_path) or not temp_folder_pattern.match(folder):
+            continue  # 跳过非温度文件夹
+
+        T = extract_temperature(folder)
+        print(f"\n处理文件夹: {folder}（温度 {T} K）")
+        if T is None:
+            print("  跳过：无法提取温度")
+            continue
+
+        # 检查文件是否存在
+        msd_path = os.path.join(folder_path, "msd_li.dat")
+        data_path = os.path.join(folder_path, "LYC.data")
+        if not os.path.exists(msd_path):
+            print(f"  跳过：缺少{msd_path}")
+            continue
+        if not os.path.exists(data_path):
+            print(f"  跳过：缺少{data_path}")
+            continue
+
+        # 解析Li数量和体积
+        try:
+            li_count, volume_angstrom3 = parse_lammps_data(data_path)
+            volume_m3 = volume_angstrom3 * angstrom3_to_m3
+            n = li_count / volume_m3
+            print(f"  Li+数量: {li_count}，盒子体积: {volume_angstrom3:.2f} ų")
+            print(f"  载流子浓度: {n:.6e} m⁻³")
+        except Exception as e:
+            print(f"  跳过：解析data文件失败: {e}")
+            continue
+
+        # 读取MSD数据
+        try:
+            time, msd = read_msd(msd_path)
+        except Exception as e:
+            print(f"  跳过：读取MSD失败: {e}")
+            continue
+
+        # 计算扩散系数
+        try:
+            D = calculate_diffusion(time, msd, t_start, t_end)
+            print(f"  扩散系数 D = {D:.6e} m²/s")
+        except Exception as e:
+            print(f"  跳过：计算扩散系数失败: {e}")
+            continue
+
+        # 计算离子电导
+        try:
+            sigma = calculate_conductivity(D, T, n)
+            print(f"  离子电导 σ = {sigma:.6e} S/m")
+        except Exception as e:
+            print(f"  跳过：计算电导失败: {e}")
+            continue
+
+        # 保存结果
+        temperatures.append(T)
+        D_list.append(D)
+        sigma_list.append(sigma)
+
+    # 绘制阿伦尼乌斯图（双拟合线）
+    if len(temperatures) >= 2:
+        print("\n===== 计算完成，绘制双拟合阿伦尼乌斯图 =====")
+        arrhenius_plot(temperatures, D_list, sigma_list)
+    else:
+        print(f"\n===== 错误：有效数据点不足（仅{len(temperatures)}个），无法绘图 =====")

corner-sharing/utils/CS_analyse.py

@@ -128,7 +128,7 @@ def CS_count(struct, shared_count, sp='Li'):
 
     return CS_count
 
-structure = Structure.from_file("data/CS_Table1/Li3Al(BO3)2_mp-6097_computed.cif")
+structure = Structure.from_file("../data/0921/wjy_001.cif")
 a = CS_catulate(structure,notice=True)
 b = CS_count(structure,a)
 print(f"{a}\n{b}")