A Perspective on Foundation Models in Chemistry

Foundation models are an emerging paradigm in artificial intelligence (AI), with successful examples like ChatGPT transforming daily workflows. Generally, foundation models are large-scale, pretrained models capable of adapting to various downstream tasks by leveraging extensive data and model scaling. Their success has inspired researchers to develop foundation models for a wide range of chemical challenges, from materials discovery to understanding structure-property relationships, areas where conventional machine learning (ML) models often face limitations. In addition, foundation models hold promise for addressing persistent ML challenges in chemistry, such as data scarcity and poor generalization. In this perspective, we review recent progress in the development of foundation models in chemistry across applications of varying scope. We also discuss emerging trends and provide an outlook on promising approaches for advancing foundation models in chemistry.