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arXiv · 2606.29100

Toward Exascale AI for Science: A Scalable AI Skill for Autonomous Microkinetics Discovery

Ken-ichi Nomura·William Dawson·Nabankur Dasgupta·Taufeq Mohammed Razakh·Thomas Linker·Kai Ito·Aiichiro Nakano
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Abstract

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.

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Ken-ichi Nomura, William Dawson, Nabankur Dasgupta, Taufeq Mohammed Razakh, Thomas Linker, Kai Ito, Aiichiro Nakano. 2026-06-27. Toward Exascale AI for Science: A Scalable AI Skill for Autonomous Microkinetics Discovery. https://arxiv.org/abs/2606.29100

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