Abstract
Artificial intelligence (AI) is poised to transform heterogeneous catalysis, opening avenues for catalytic materials discovery. By uncovering intricate patterns in high-dimensional data, AI has been reshaping our pursuit of sustainable catalytic processes across the energy, environmental and chemical sectors. This promise, however, hinges on overcoming fundamental barriers, including limitations in data availability and quality, challenges in the generalizability and interpretability of data-augmented decisions, and the persistent gap between in silico predictions and experiments. Here we outline a forward-looking roadmap for deeply integrating AI into heterogeneous catalysis with an AI-ready data ecosystem, multimodal foundation models, and ultimately autonomous laboratories to accelerate the development of next-generation catalytic technologies via AI-empowered human–machine collaboration. (Figure presented.)
| Original language | English |
|---|---|
| Pages (from-to) | 102-111 |
| Number of pages | 10 |
| Journal | Nature Catalysis |
| Volume | 9 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 2026 |
Fingerprint
Dive into the research topics of 'Roadmap for transforming heterogeneous catalysis with artificial intelligence'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver