Papers by Zhichen Tang

1 papers
EvoMD-LLM: Learning the Language of Species Evolution in Reactive Molecular Dynamics (2026.findings-acl)

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Challenge: Existing models operate on static molecular representations or rely on external tools for reasoning.
Approach: They propose a framework that reformulates species-level molecular dynamics as a symbolic temporal language modeling problem.
Outcome: The proposed model outperforms neural networks and language-based baselines on multiple temporal prediction tasks and generates plausible interpretations of reaction dynamics.

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