Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering· 从原子到过程：AI 和机器学习在化工中的角色
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 un
综述 AI 和机器学习在化工领域的应用进展。
- 核心方法
- 将 AI 和机器学习方法与基于原理的模型相结合，以解决化工问题中的预测准确性、数据稀缺性、外推能力、可解释性和模型生命周期管理等问题。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 化工领域的多个环节，如原子尺度模拟、材料和催化剂发现等，面临预测准确性、数据稀缺、外推能力不足等挑战。
- 关键实验
- 未提供
- 主要贡献
- 提供了一种将 AI/ML 与传统化工模型融合的新视角，展示了该领域内代表性的应用案例和发展趋势。
- 意义与局限
- 加深了化工领域对 AI 和机器学习的应用理解，促进了跨学科的融合，可能推动化工领域的技术革新和发展。局限性在于某些应用的可解释性和可靠性仍需进一步研究。