Indeed, there is a significant structure consistency between common data and microscopic particles, as both can be represented across multiple kinds of dimensions: Vision focuses on effectively modeling the 3D structures of microscopic particles and recognition their attributes, functions, properties, etc. 2) Microscopic Artificial Intelligence Generated Content (MicroAIGC) is dedicated to generating expected microstructures based on specified requirements or conditions. 3) Microscopic Multimodality aims to effectively align and capture the structure of microscopic particles from different modalities. Among these, Microscopic Multimodal Large Language Model (MicroMLLM) shows significant potential for MicroAI applications. 4) Microscopic Route Planning (Retrosynthesis) aims at synthesizing target microstructures. 5) Microscopic Dataand-Knowledge-Driven Artificial Intelligence seeks to integrate the knowledge and rules from science into deep learning. This integration enhances MicroAI capabilities and reduces dependence on extensive data sets.