arXiv:2606.05050v1 Announce Type: cross Abstract: Theoretical heterogeneous catalysis promises rapid catalyst discovery, yet computational and machine-learning predictions often deviate from experiment and stay confined to narrow material families, for want of a faithful, condition-aware catalytic simulator. We present CatDT (Catalysis Digital Twin), a self-evolving multi-agent system that builds an autonomous digital twin of a working catalyst, unifying gas-solid and liquid-solid modeling. From only a bulk crystal and a natural-language reaction description, eight specialized agents and 27 sc
Advances in AI, particularly multi-agent systems and natural language processing, are enabling the development of sophisticated digital twins capable of autonomous scientific discovery.
This development could dramatically accelerate innovation in materials science and chemistry, reducing the time and cost associated with discovering new catalysts essential for various industrial processes.
The traditional, largely experimental and iterative process of catalyst discovery is evolving into an AI-driven, simulation-first approach, potentially leading to faster and more efficient material development.
- · Chemical industry
- · Materials science sector
- · AI software developers
- · Pharmaceuticals
- · Traditional experimental labs (less competitive)
- · Companies slow to adopt AI-driven R&D
Rapid discovery of more efficient and sustainable catalysts for energy, manufacturing, and environmental applications.
Reduced dependence on rare or expensive raw materials by designing catalysts that utilize more abundant elements.
The development of entirely new chemical processes and products that were previously unfeasible due to catalyst limitations.
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Read at arXiv cs.AI