Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models

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…

• UA
  University at Buffalo
• JS
  Japan Society for the Promotion of Science

  Awards: JP25H01508, JP25K17991, JP25K01737