Spectroscopic techniques are essential tools for determining the structure of molecules.

It is common that these data themselves have inherently complicated 3D geometric structures.

It is common that these data themselves have inherently complicated 3D geometric structures.

Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been applied in various kinds of areas such as science, finance and software engineering.

The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research.

However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making experimental validation difficult.

Recently, several equivariant message passing neural networks (MPNNs) have been shown to outperform models built using otherapproaches intermsofaccuracy.