with open('poscars.txt', 'r') as f: poscars = f.readlines()，更改两行代码，不用pymatgen，使得代码能读取目录下所有POSCAR结构文件

好的，下面是修改后的代码，它使用Python内置的open函数读取目录下的所有POSCAR文件，然后解析文件内容计算C原子的分布规律，并使用KMeans算法进行聚类。

import os
import numpy as np

# 获取目录下所有POSCAR文件的路径
poscars = [os.path.join(root, file) for root, dirs, files in os.walk('.') for file in files if file.endswith('.POSCAR')]

# 计算每个文件中C原子的分布规律
c_coords = []
for poscar in poscars:
    with open(poscar, 'r') as f:
        lines = f.readlines()
        lattice = np.array([list(map(float, line.split())) for line in lines[2:5]])
        positions = np.array([list(map(float, line.split()[:3])) for line in lines[8:]])
        c_indices = [i for i, line in enumerate(lines[8:]) if 'C' in line]
        c_positions = positions[c_indices]
        c_coords.append([len([j for j, pos in enumerate(c_positions) if np.linalg.norm(pos - c_positions[i]) < 2]) for i in range(len(c_positions))])

# 将C原子分布规律转换为numpy数组
c_coords = np.array(c_coords)

# 定义KMeans算法
def kmeans(X, k):
    centroids = X[np.random.choice(range(len(X)), k, replace=False)]
    while True:
        clusters = [[] for i in range(k)]
        for x in X:
            distances = np.linalg.norm(x - centroids, axis=1)
            cluster = np.argmin(distances)
            clusters[cluster].append(x)
        new_centroids = [np.mean(cluster, axis=0) for cluster in clusters]
        if np.array_equal(new_centroids, centroids):
            break
        centroids = new_centroids
    return clusters

# 使用KMeans算法进行聚类
clusters = kmeans(c_coords, 3)

# 输出每个簇中的文件名
for i, cluster in enumerate(clusters):
    print('Cluster {}:'.format(i))
    for j in range(len(poscars)):
        with open(poscars[j], 'r') as f:
            lines = f.readlines()
            lattice = np.array([list(map(float, line.split())) for line in lines[2:5]])
            positions = np.array([list(map(float, line.split()[:3])) for line in lines[8:]])
            c_indices = [i for i, line in enumerate(lines[8:]) if 'C' in line]
            c_positions = positions[c_indices]
            c_counts = [len([j for j, pos in enumerate(c_positions) if np.linalg.norm(pos - c_positions[i]) < 2]) for i in range(len(c_positions))]
            if c_counts in cluster:
                print(poscars[j])
    print()

这里使用了Python内置的open函数读取POSCAR文件，然后解析文件内容计算C原子的分布规律。其余部分与前面的代码相同。