arXiv:2605.29394v1 Announce Type: new Abstract: While large language models (LLMs) excel at static scientific reasoning, they struggle to model the temporal structure of dynamic physical processes. We present EvoMD-LLM (Evolutionary Molecular Dynamics Large Language Model), a framework that reformulates species-level molecular dynamics as a symbolic temporal language modeling problem. Reactive MD trajectories are discretized into sequences of molecular events, where each token represents a chemical species augmented with its persistence duration, enabling standard autoregressive LLMs to learn
The increasing sophistication of LLMs and the demand for better modeling of dynamic scientific processes, combined with progress in discretizing complex simulations, makes this a natural evolution in AI application.
This development allows LLMs to move beyond static reasoning to accurately model and predict dynamic chemical and biological processes, which has implications for drug discovery, material science, and synthetic biology.
LLMs' utility expands from text and static data to temporal and event-driven scientific simulations, enabling AI to 'learn the language' of physical evolution rather than just descriptive knowledge.
- · AI researchers in scientific modeling
- · Pharmaceutical industry
- · Materials science
- · Synthetic biology companies
- · Traditional molecular dynamics simulation techniques
- · Companies reliant on purely static AI models for scientific discovery
AI models will become more adept at predicting and designing novel molecules and biological pathways.
Accelerated discovery of new drugs, catalysts, and biomaterials could lead to significant industrial innovation and economic shifts.
The ability to simulate and predict species evolution at a molecular level could unlock new possibilities in fundamental biological understanding and directed evolution.
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Read at arXiv cs.AI