arXiv:2601.13508v4 Announce Type: replace-cross Abstract: 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
The proliferation of advanced AI models and agentic architectures is enabling new forms of scientific automation previously unimaginable.
This development indicates a significant acceleration in autonomous scientific discovery, potentially collapsing research cycles and dramatically speeding up innovation in fields like materials science and chemistry.
The paradigm shifts from human-driven, tool-assisted research to self-sustaining agentic systems capable of iterative design and experimentation.
- · Materials science research institutions
- · Chemical companies
- · AI platform providers
- · Catalysis industry
- · Traditional drug discovery models
- · Manual experimental research labs
Accelerated discovery and optimization of new catalysts and materials.
Reduced R&D costs and shortened time-to-market for advanced materials and industrial processes.
The emergence of entirely new industries and products based on agentically discovered materials with novel properties.
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Read at arXiv cs.AI