EvoMD-LLM: Learning the Language of Species Evolution in Reactive Molecular Dynamics

ACL ARR 2026 January Submission5451 Authors

Keywords: AI for Science, Large Language Models, Molecular Dynamics, Temporal Sequence Prediction, Scientific Reasoning

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 compositional evolution over time through efficient fine-tuning. A key component of EvoMD-LLM is temporal scaffolding, which treats event duration as an explicit linguistic token and serves as a structured inductive bias, significantly reducing invalid or hallucinated molecular outputs compared to conventional sequence modeling approaches. We evaluate EvoMD-LLM on multiple temporal prediction tasks, achieving up to 68.66\% accuracy and consistently outperforming sequential neural networks and language-based baselines. Beyond quantitative improvements, we qualitatively observe that the model can generate plausible physical interpretations of reaction dynamics, despite not being explicitly trained for explanation. These results demonstrate that symbolic temporal language modeling provides an effective framework for grounding LLMs in dynamic physical simulations.

Paper Type: Long

Research Area: Language Models

Research Area Keywords: applications, fine-tuning, neurosymbolic approaches, LLM/AI agents

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 5451