Researchers at Tohoku University have outlined a transformative approach to catalyst discovery in a review published in Angewandte Chemie International Edition. The study details how integrating large AI models with universal machine learning interatomic potentials (MLIPs) and large language models (LLMs) is replacing traditional trial-and-error methods with a predictive, data-driven workflow. Catalysts, materials essential for accelerating chemical reactions in applications like fuel cells and hydrogen production, have historically required years of laborious laboratory testing to identify viable candidates. The new strategy allows scientists to simulate atomic interactions and predict performance before physical synthesis occurs. By combining high-quality catalysis databases with advanced AI tools, researchers can explore vast chemical spaces with unprecedented speed and accuracy. Universal MLIPs enable precise simulations of how atoms behave, while LLMs analyze scientific literature to interpret complex knowledge and suggest new research directions. This integration connects computational modeling, experimental validation, and scientific concepts into a unified system. The approach significantly accelerates discovery by allowing large-scale simulations and rapid identification of promising designs. In some cases, AI systems can guide their own subsequent research steps, creating a self-improving cycle of learning. Hao Li, a distinguished professor at Tohoku University's WPI-AIMR, states that this integration moves catalyst discovery from a slow, incremental process to a continuously accelerating one. Looking ahead, the team envisions fully integrated, AI-powered closed-loop platforms where prediction, synthesis, testing, and learning operate in a continuous feedback cycle. These systems aim to reduce wasted resources while increasing the probability of breakthrough discoveries. Beyond catalysis, the researchers plan to apply these strategies to other critical materials fields, including batteries and hydrogen storage, by building cross-disciplinary digital ecosystems. The review marks a milestone in AI-driven materials science and highlights the emerging era where materials discovery is not only faster but perpetually accelerating. This shift promises to dramatically shorten the timeline between scientific insight and real-world application in clean energy and sustainable technologies.