Commercialization path of data-driven green catalytic technology: the application of machine learning in technology...

In the context of the global strategy of sustainable development and carbon neutrality, green catalytic technology, as the core of achieving efficient and clean chemical conversion, has a complex and uncertain path from laboratory innovation to large-scale commercial application. The traditional R&D and commercialization models are often limited by bottlenecks such as high trial and error costs, long cycles, and difficulties in coupling and analyzing multi-scale factors. This review is based on the perspective of technology lifecycle management and systematically explores how data-driven paradigms, especially machine learning methods, can profoundly reshape the full chain management logic of green catalytic technology from conceptual design, process development, engineering scaling up to market deployment. The article first analyzes the core challenges and data requirements of each stage of commercialization of green catalytic technology (basic research, concept validation, process optimization, pilot scale, commercial operation), pointing out that it is essentially a complex system optimization problem with multiple objectives and constraints. Furthermore, this article provides an in-depth overview of the cutting-edge applications and typical cases of machine learning in key areas such as intelligent design and screening of catalytic materials (such as high-throughput virtual screening, structure-activity relationship modeling), reaction mechanism analysis and kinetic simulation, intelligent optimization of reactor design and process conditions, as well as full lifecycle environmental impact and economic technology analysis. The advantages and limitations of different paradigms such as supervised learning, unsupervised learning, reinforcement learning, and generative models in solving specific problems were analyzed in detail. Finally, this article critically summarizes the common challenges faced by the current data-driven path, including the scarcity of high-quality datasets, interpretability and physical consistency of models, and integration difficulties in cross scale modeling. It also looks forward to future research directions, such as physical information machine learning that integrates domain knowledge, the construction of standardized data platforms, and the development of intelligent decision support systems for human-machine collaboration. This review aims to provide a systematic framework and forward-looking guidance for interdisciplinary research in the fields of catalytic science, chemical engineering, and data science, accelerating the commercialization of green catalytic technology in a more efficient and predictable manner, and serving the construction of green manufacturing systems.