Autonomous Computational Catalysis Research via Agentic Systems

arXiv:2601.13508v2 Announce Type: replace-cross
Abstract: Fully automating the scientific process is a transformative ambition in materials science, yet current artificial intelligence masters isolated workflow fragments. In computational catalysis, a system autonomously navigating the entire research lifecycle from conception to a scientifically meaningful manuscript remains an open challenge. Here we present CatMaster, a catalysis-native multi-agent framework that couples project-level reasoning with the direct execution of atomistic simulations, machine-learning modelling, literature analysis, and manuscript production within a unified autonomous architecture. Across progressively demanding evaluations, CatMaster achieves perfect scores on four end-to-end short-form catalysis scenarios, reaches near-leaderboard performance on five of six MatBench tasks, performs self-discovery of reaction mechanisms grounded in literature or from scratch, and executes a fully closed-loop single-atom catalyst design problem. Together, these results show that end-to-end autonomous computational catalysis is now practical for research programmes, while highlighting that bridging the gap to genuine scientific closure requires tighter integration with reliable physical engines and domain-rigorous methodologies.