# Toward Automated Simulation Research Workflow through LLM Prompt Engineering Design

arXiv: https://arxiv.org/abs/2408.15512 · HTML: https://mlsanity.com/paper/2408.15512/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022)
- [GuacaMol: Benchmarking Models for De Novo Molecular Design](https://mlsanity.com/paper/1811.09621/) (2018)
- [Artificial Intelligence for Literature Reviews: Opportunities and Challenges](https://mlsanity.com/paper/2402.08565/) (2024)
- [Automated Paper Screening for Clinical Reviews Using Large Language Models](https://mlsanity.com/paper/2305.00844/) (2023)

## Built on by (most cited first, 10 of 10)

- [Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations](https://mlsanity.com/paper/2505.08195/) (2025)
- [SimuGen: Multi-modal Agentic Framework for Constructing Block Diagram-Based Simulation Models](https://mlsanity.com/paper/2506.15695/) (2025)
- [SoM-1K: A Thousand-Problem Benchmark Dataset for Strength of Materials](https://mlsanity.com/paper/2509.21079/) (2025)
- [PolyJarvis: An LLM-Orchestrated Agent for Automated All-Atom Molecular Dynamics of Amorphous Homopolymers](https://mlsanity.com/paper/2604.02537/) (2026)
- [Large Language Model Agent for User-friendly Chemical Process Simulations](https://mlsanity.com/paper/2601.11650/) (2026)
- [Automated Retrosynthesis Planning of Macromolecules Using Large Language Models and Knowledge Graphs](https://mlsanity.com/paper/2501.08897/) (2025)
- [Modular AI-Powered Interviewer with Dynamic Question Generation and Expertise Profiling](https://mlsanity.com/paper/2601.11534/) (2025)
- [Towards Discovery of Polymers for Insulin Delivery via Physics-Grounded Agentic Workflows](https://mlsanity.com/paper/2605.18831/) (2026)
- [An LLM-Orchestrated Agent for Directional-Coupler Design with Self-Consistent Eigenmode and FDTD Validation](https://mlsanity.com/paper/2606.22493/) (2026)
- [LLM Agents Perform Controlled Experiments Using Simulation Models](https://mlsanity.com/paper/2608.23622/) (2026)

## Abstract (arXiv)

The advent of Large Language Models (LLMs) has created new opportunities for the automation of scientific research spanning both experimental processes and computational simulations. This study explores the feasibility of constructing an autonomous simulation agent (ASA) powered by LLMs through prompt engineering and automated program design to automate the entire simulation research process according to a human-provided research plan. This process includes experimental design, remote upload and simulation execution, data analysis, and report compilation. Using a well-studied simulation problem of polymer chain conformations as a test case, we assessed the long-task completion and reliability of ASAs powered by different LLMs, including GPT-4o, Claude-3.5, etc. Our findings revealed that ASA-GPT-4o achieved near-flawless execution on designated research missions, underscoring the potential of methods like ASA to achieve automation in simulation research processes to enhance research efficiency. The outlined automation can be iteratively performed for up to 20 cycles without human intervention, illustrating the potential of ASA for long-task workflow automation. Additionally, we discussed the intrinsic traits of ASA in managing extensive tasks, focusing on self-validation mechanisms, and the balance between local attention and global oversight.