发表机构
The University of Texas at Austin; Northwestern University; University of Delaware; Baker Hughes; Linde plc; Schneider Electric; Boston University; Universitat Rovira i Virgili; Georgia Institute of Technology; University of Wisconsin; National University of Singapore（德克萨斯大学奥斯汀分校; 西北大学; 特拉华大学; 贝克休斯公司; 林德公司; 施耐德电气; 波士顿大学; 罗维拉-威尔吉利大学; 佐治亚理工学院; 威斯康星大学; 新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述AI/ML在化学工程中从原子到过程的应用，强调与第一性原理结合的混合物理信息框架，以提升预测精度和可靠性，并指出AI/ML增强而非取代核心原理。
AI 中文摘要
人工智能（AI）和机器学习（ML）的快速成熟催化了化学工程问题在表述、分析和解决方式上的深刻转变。计算能力、数据可用性和学习算法的进步使得AI/ML方法能够影响从原子尺度模拟、材料和催化剂发现、传递与热力学、分离过程、过程系统工程到工业操作等广泛的应用领域。本文对近期方法论发展和代表性应用进行了展望，强调了AI/ML工具如何与基于第一性原理的模型集成，以应对预测精度、数据稀缺性、外推能力、可解释性和模型生命周期管理等方面的挑战。在各个领域中，一个统一的趋势是从纯黑箱方法转向混合和物理信息框架，这些框架明确尊重守恒定律、热力学一致性以及已知的结构约束。这些方法不仅提高了鲁棒性和可靠性，还通过为任务和决策情境提供适当抽象层次的信息，实现了有意义的人机协作。我们得出结论，AI和ML并未取代化学工程的核心原理，而是对其进行了增强。随着该领域向日益自主、自适应和可持续的系统发展，将AI/ML与第一性原理理解和领域专业知识进行深思熟虑的整合，对于在研究和工业实践中充分发挥其潜力至关重要。
英文摘要
The rapid maturation of artificial intelligence (AI) and machine learning (ML) has catalyzed a profound shift in how chemical engineering problems are formulated, analyzed, and solved. Advances in computing, data availability, and learning algorithms have enabled AI/ML methods to impact applications spanning atomic-scale simulations, materials and catalyst discovery, transport and thermodynamics, separations, process systems engineering, and industrial operations. This article provides a perspective on recent methodological developments and representative applications, emphasizing how AI/ML tools are being integrated with first-principles models to address challenges of predictive accuracy, data scarcity, extrapolation, interpretability, and model lifecycle management. Across domains, a unifying trend is the move away from purely black-box approaches toward hybrid and physics-informed frameworks that explicitly respect conservation laws, thermodynamic consistency, and known structural constraints. These approaches not only improve robustness and reliability, but also enable meaningful human-AI collaboration by providing information at an appropriate level of abstraction for the task and decision context. We conclude that AI and ML are not replacing the core principles of chemical engineering; rather, they are amplifying them. As the field advances toward increasingly autonomous, adaptive, and sustainable systems, the thoughtful integration of AI/ML with first-principles understanding and domain expertise will be essential to realizing their full potential across both research and industrial practice.
