.png){kind=icon}
.png){kind=icon}
Return
Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models
.png){kind=icon}
OA
AI
DOI：10.1002/anie.202526150.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The integration of artificial intelligence (AI) into catalysis is fundamentally reshaping the research paradigm of catalyst discovery. Unlike traditional trial-and-error approaches, AI-empowered data-driven technologies, particularly large AI models such as universal machine learning interatomic potentials (MLIPs) and large language models (LLMs), offer unprecedented capabilities in exploring complex spaces, predicting catalytic performance, and accelerating rational design. Standing at the forefront of data-driven science, we underscore how databases, universal MLIPs, and LLMs are revolutionizing the traditional catalysis paradigm and bridging the ontology-concept-computation-experiment continuum. We then demonstrate significant recent progress, and discuss their potential and challenges in the catalytic field. By leveraging cutting-edge universal MLIPs and LLMs, researchers can conduct large-scale simulations, highly efficient data acquisition, training, and prediction, and even self-directed research in the field of catalysis. Looking ahead, these advantages enable the rapid development of target catalysts, which will be propelled by integrated universal MLIPs, multimodal LLMs, and automation systems. Developments in these domains will pave the way toward AI-empowered closed-loop platforms and cross-disciplinary Digital Materials Ecosystems that broaden the discovery landscape and foster cross-materials innovation, marking the dawn of a new era in which catalyst materials discovery is perpetually accelerating.
Keywords:
AI for catalysis
artificial intelligence (AI)
data-science
large language models
machine learning interatomic potentials
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
16.9
Papers:
4.7K
Citations:
368
View more organizations