通用模型让小数据化学研究也能高效突破。
General-Purpose Models for the Chemical Sciences: LLMs and Beyond
- 用通用模型处理化学领域小而杂的数据集
- 在少量数据下完成跨任务灵活学习
- 适合关注化学智能的科研人员与开发者
数据驱动技术在化学科学中具有巨大潜力，可加速研究进程。但化学领域存在数据多样、样本量小、边界模糊等问题，传统机器学习难以有效应用。近年来，通用模型（GPMs）如大语言模型展现出未直接训练的任务解决能力，且能以低数据量、多格式灵活运作。本文探讨GPMs的核心构建原理，并综述其在化学全链条研究中的最新与前沿应用。尽管多数应用仍处原型阶段，我们预计未来几年随着关注度提升，这些技术将逐步成熟。
原文摘要 · Abstract (English)
Data-driven techniques have a large potential to transform and accelerate the chemical sciences. However, chemical sciences also pose the unique challenge of very diverse, small, fuzzy datasets that are difficult to leverage in conventional machine learning approaches. A new class of models, which can be summarized under the term general-purpose models (GPMs) such as large language models, has shown the ability to solve tasks they have not been directly trained on, and to flexibly operate with low amounts of data in different formats. In this review, we discuss fundamental building principles of GPMs and review recent and emerging applications of those models in the chemical sciences across the entire scientific process. While many of these applications are still in the prototype phase, we expect that the increasing interest in GPMs will make many of them mature in the coming years.
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