Toward Exascale AI for Science: A Scalable AI Skill for Autonomous Microkinetics Discovery
For materials scientists, this work provides an AI skill that automates complex microkinetics workflows, but the results are qualitative and lack concrete performance numbers, making it an incremental step toward autonomous discovery.
The paper presents a scalable AI-driven framework for autonomous microkinetics discovery that reduces expert intervention, recovers from failed simulations, and systematically evaluates surrogate model reliability, demonstrating robust and scalable capabilities for materials research.
We present a scalable AI-driven framework that advances autonomous scientific discovery by combining agentic workflow automation, high-performance computing, and scientific surrogate models. Using microkinetics discovery as a testbed, the work demonstrates how AI can reduce expert intervention, recover from failed simulations, and systematically evaluate surrogate model reliability. This study shows how AI skills can transform complex domain workflows into robust, scalable capabilities for next-generation materials research.