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arXiv AI By Zhilong Song, Zongmin Zhang, Lixue Cheng

Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin

Read the original on arXiv AI →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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