AI系统自主完成催化剂设计全流程，从问题到方案闭环迭代。

Autonomous agents are beginning to transform scientific research from tool-assisted workflows toward self-sustaining discovery processes. Computational catalysis provides a representative challenge, as catalyst discovery requires high-level questions to be translated into coordinated model construction, atomistic simulation, mechanistic analysis, and iterative design across multiple scales. Here we introduce CatMaster, a catalysis-native agentic research system that recasts computational catalysis as a low-barrier virtual ecosystem for autonomous research. CatMaster maintains an evolving research state and extends capabilities through self-feedback across model construction, calculation, critique and catalyst-design decisions within one extensible environment. Across progressively challenging tasks, CatMaster converts natural-language requests into concrete computational studies, from essential atomistic modelling and standard calculations to mechanism exploration and closed-loop catalyst design. It showed robust execution in representative computational-catalysis scenarios and near-leading performance across selected MatBench tasks, with phonons scenario demonstrating its modelling self-evolution capability. In the independent CO2-to-CO catalyst design case, CatMaster used iterative self-critique and evidence refinement to identify competitive B-CoN4 and NiN3B/N-NiN3B motifs. These results establish a virtual-ecosystem paradigm in which AI agents move beyond simulation execution toward end-to-end computational research, providing a foundation for autonomous discovery in catalysis and materials science.