MDAgent2: Large Language Model for Code Generation and Knowledge Q&A in Molecular Dynamics
Molecular dynamics (MD) simulations are essential for understanding atomic-scale behaviors in materials science, yet writing LAMMPS scripts remains highly specialized and time-consuming tasks. Although LLMs show promise in code generation and domain-specific question answering, their performance in MD scenarios is limited by scarce domain data, the high deployment cost of state-of-the-art LLMs, and low code executability. Building upon our prior MDAgent, we present MDAgent2, the first end-to-end framework capable of performing both knowledge Q&A and code generation within the MD domain. We construct a domain-specific data-construction pipeline that yields three high-quality datasets spanning MD knowledge, question answering, and code generation. Based on these datasets, we adopt a three stage post-training strategy--continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL)--to train two domain-adapted models, MD-Instruct and MD-Code. Furthermore, we introduce MD-GRPO, a closed-loop RL method that leverages simulation outcomes as reward signals and recycles low-reward trajectories for continual refinement. We further build MDAgent2-RUNTIME, a deployable multi-agent system that integrates code generation, execution, evaluation, and self-correction. Together with MD-EvalBench proposed in this work, the first benchmark for LAMMPS code generation and question answering, our models and system achieve performance surpassing several strong baselines.This work systematically demonstrates the adaptability and generalization capability of large language models in industrial simulation tasks, laying a methodological foundation for automatic code generation in AI for Science and industrial-scale simulations. URL: https://github.com/FredericVAN/PKU_MDAgent2
695d04eac03d6d81e4399cd9
Zhuofan Shi
claimed_verified
2026-01-07T09:26:56.695Z
65d7018c90f11951bcfdf2f5
https://cdn-avatars.huggingface.co/v1/production/uploads/65d7018c90f11951bcfdf2f5/RU0-n9k3RIdtv8xvg9Upe.jpeg
Zhuofan Shi
FredericFan
user
FredericFan
695d04eac03d6d81e4399cda
Hubao A
695d04eac03d6d81e4399cdb
Yufei Shao
695d04eac03d6d81e4399cdc
Mengyan Dai
695d04eac03d6d81e4399cdd
Yadong Yu
695d04eac03d6d81e4399cde
Pan Xiang
695d04eac03d6d81e4399cdf
Dongliang Huang
695d04eac03d6d81e4399ce0
Hongxu An
695d04eac03d6d81e4399ce1
Chunxiao Xin
695d04eac03d6d81e4399ce2
Haiyang Shen
695d04eac03d6d81e4399ce3
Zhenyu Wang
695d04eac03d6d81e4399ce4
Yunshan Na
695d04eac03d6d81e4399ce5
Gang Huang
695d04eac03d6d81e4399ce6
Xiang Jing
MDAgent2: Large Language Model for Code Generation and Knowledge Q&A in Molecular Dynamics
Molecular dynamics (MD) simulations are essential for understanding atomic-scale behaviors in materials science, yet writing LAMMPS scripts remains highly specialized and time-consuming tasks. Although LLMs show promise in code generation and domain-specific question answering, their performance in MD scenarios is limited by scarce domain data, the high deployment cost of state-of-the-art LLMs, and low code executability. Building upon our prior MDAgent, we present MDAgent2, the first end-to-end framework capable of performing both knowledge Q&A and code generation within the MD domain. We construct a domain-specific data-construction pipeline that yields three high-quality datasets spanning MD knowledge, question answering, and code generation. Based on these datasets, we adopt a three stage post-training strategy--continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL)--to train two domain-adapted models, MD-Instruct and MD-Code. Furthermore, we introduce MD-GRPO, a closed-loop RL method that leverages simulation outcomes as reward signals and recycles low-reward trajectories for continual refinement. We further build MDAgent2-RUNTIME, a deployable multi-agent system that integrates code generation, execution, evaluation, and self-correction. Together with MD-EvalBench proposed in this work, the first benchmark for LAMMPS code generation and question answering, our models and system achieve performance surpassing several strong baselines.This work systematically demonstrates the adaptability and generalization capability of large language models in industrial simulation tasks, laying a methodological foundation for automatic code generation in AI for Science and industrial-scale simulations. URL: https://github.com/FredericVAN/PKU_MDAgent2
MDAgent2 enables automated molecular dynamics code generation and question answering through domain-adapted language models and a multi-agent runtime system.
LAMMPS molecular dynamics code generation question answering continued pre-training supervised fine-tuning reinforcement learning MD-Instruct MD-Code MD-GRPO MDAgent2 MD-EvalBench