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PERSPECTIVE article

Front. Chem. Eng.
Sec. Computational Methods in Chemical Engineering
Volume 6 - 2024 | doi: 10.3389/fceng.2024.1458156
This article is part of the Research Topic Editors’ Showcase: Computational Methods in Chemical Engineering View all articles

Generative Artificial Intelligence in Chemical Engineering Spans Multiple Scales

Provisionally accepted
Fengqi You Fengqi You *Benjamin Decardi-Nelson Benjamin Decardi-Nelson Abdulelah S. Alshehri Abdulelah S. Alshehri
  • Cornell University, Ithaca, United States

The final, formatted version of the article will be published soon.

    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.

    Keywords: artificial intelligence - AI, generative learning, quantum-chemical calculations, materials, Process engineering

    Received: 02 Jul 2024; Accepted: 12 Aug 2024.

    Copyright: © 2024 You, Decardi-Nelson and Alshehri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Fengqi You, Cornell University, Ithaca, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.