Hierarchical incremental learning deciphers molecular arrangements in multi-component materials

# Sample datasets ### data format: >dataSet_count.npy: A numpy array of size N1, consisting of integers from 1 to N1. >dataSet_dist.npy: A numpy matrix of shape (N1, M1, M1). The size of the Coulombic matrix is M1 × M1. >dataSet_name.npy: A numpy matrix of shape (N1, M1). Every row contains the atom set names. ### task A md1 md2 md3 md4 md5 md6 ### task B md1 md2 md3 ### task C md1 md2 md3 ### task D md1 md2 ### task E md1 md2 md3 md4 ### task F md1 md2 md3 ### task G md1 md2