Generative artificial intelligence in chemical engineering spans multiple scales

Abstract

Recent advances in generative artificial intelligence (GenAI), particularly large language models (LLMs), are profoundly impacting many fields. In chemical engineering, GenAI plays a pivotal role in the design, scale-up, and optimization of chemical and biochemical processes. The natural language understanding capabilities of LLMs enable the interpretation of complex chemical and biological data. Given the rapid developments of GenAI, this paper explores the extensive applications of GenAI in multiscale chemical engineering, spanning from quantum mechanics to macro-level optimization. At quantum and molecular levels, GenAI accelerates the discovery of novel products and enhances the understanding of fundamental phenomena. At larger scales, GenAI improves process design and operational efficiency, contributing to sustainable practices. We present several examples to demonstrate the role of GenAI, including its impact on nanomaterial hardness enhancement, novel catalyst generation, protein design, and the development of autonomous experimental platforms. This multiscale integration demonstrates the potential of GenAI to address complex challenges, drive innovation, and foster advancements in chemical engineering.