AI 中文总结
研究探索大语言模型用于学习分子几何结构语言的微调方法，利用笛卡尔坐标或Z矩阵微调，能准确预测小分子平衡结构等，Z矩阵效果更好，且混入自然语言对微调可增强预测能力并保留预训练语言能力。
AI 中文摘要
大语言模型（LLMs）的强大功能促使我们研究如何对其进行微调以学习“分子几何语言”。使用笛卡尔坐标或Z矩阵对LLMs进行微调，为准确预测小型有机和类药物分子的平衡结构及多样构象集提供了极其简单的方法，具有出色的准确性且优于专门的深度学习模型。虽然分子几何结构最常见的表示形式笛卡尔坐标表现良好，但我们发现，以Z矩阵表示的几何结构的固有不变性和关系性质为LLMs适应提供了更好的语法。最后，我们表明，通过在微调中随机混入少量自然语言提示-响应对，增强LLMs对小分子几何结构的稳健预测能力，仍能保留其几乎所有的预训练语言能力。
英文摘要
The power of Large Language Models (LLMs) has led us to investigate how they might be fine-tuned for learning the "language of molecular geometry". The fine-tuning of the LLMs using Cartesian coordinates or Z-matrices provides an extremely simple method for accurately predicting equilibrium structures and diverse sets of conformers of small organic and drug-like molecules with excellent accuracy and outperforming specialized deep learning models. While the most common representation of molecular geometries are Cartesian coordinates performs adequately, we find that the inherent invariances and relational nature of geometries represented as Z-matrices provides a better grammar for LLM adaptation. Finally, we show that enhancing an LLMs capabilities for robust prediction of small molecule geometries still retains nearly all of its pre-trained language abilities by randomly mixing in small quantities of natural language prompt-response pairs into the fine-tuning.
