用AI打通催化反应机理与观测数据的联系，实现自动化的可解释研究。
Prospects for Using Artificial Intelligence to Understand Intrinsic Kinetics of Heterogeneous Catalytic Reactions
- 结合机器学习力场与多尺度建模，快速探索化学空间
- 利用原位数据提升对反应动力学的实时洞察
- 生成式AI实现模型自驱动，适合催化机理研究者
人工智能正加速异相催化研究中的模拟与材料发现。核心挑战在于将本征动力学与可观测现象建立可靠关联，即解决“多对一”问题。机器学习力场、微动力学与反应器建模的进步，使化学空间的快速探索成为可能；而原位与瞬态实验数据提供了前所未有的机制洞察。然而，数据质量不一与模型复杂度高限制了机理发现。生成式与智能体式AI可自动构建模型、量化不确定性，并实现理论与实验的闭环耦合，推动形成具备可解释性、可复现性和可迁移性的‘自驱动模型’，从而深化对催化系统的理解。
原文摘要 · Abstract (English)
Artificial intelligence (AI) is influencing heterogeneous catalysis research by accelerating simulations and materials discovery. A key frontier is integrating AI with multiscale models and multimodal experiments to address the "many-to-one" challenge of linking intrinsic kinetics to observables. Advances in machine-learned force fields, microkinetics, and reactor modeling enable rapid exploration of chemical spaces, while operando and transient data provide unprecedented insight. Yet, inconsistent data quality and model complexity limit mechanistic discovery. Generative and agentic AI can automate model generation, quantify uncertainty, and couple theory with experiment, realizing "self-driving models" that produce interpretable, reproducible, and transferable understanding of catalytic systems.
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