Atomtransmachine: An atomic feature representation model for machine learning

Abstract

In this study, a self-monitoring model is proposed to extract the atomic characteristics of the main group elements and transition metals from several molecular structures. Different from previous studies, we use a spatial convolution layer to extract the spatial features of atoms and a multi-attention mechanism to screen their important features in forming new crystal structures. Extensive numerical analyses show that the features extracted using the proposed model are effective and can improve the efficiency of machine learning algorithms.