Moore’s Law for Molecular Dynamics Simulations: An Initial Study

Abstract

High-performance computing (HPC) is essential to modern molecular dynamics (MD) simulations. While computing capabilities continue to grow, it remains unclear how these advances translate into everyday scientific practice. Additional resources may be used to reduce time-to-solution, improve model fidelity, simulate larger systems, extend simulated time scales, or increase the number of independent runs. Understanding how users allocate increased computational capability is important for future system design. To investigate how MD usage has evolved over time, we analyzed over 400 publications reporting all-atom MD simulations using a large language model (LLM)-based extraction workflow. Thirty of these publications were manually reviewed to develop and validate an expert-guided extraction rubric. Using this rubric, we evaluated several LLM configurations for extracting system size, simulation duration, and number of independent runs. Extraction quality improved across successive model generations, from GPT-4o to GPT-5 and then to GPT-5.4. Within the GPT-5.4 family, accuracy decreased as model size was reduced from GPT-5.4 to GPT-5.4-mini and then to GPT-5.4-nano. However, increasing reasoning effort from medium to high made GPT-5.4-nano performance comparable to GPT-5.4 while reducing cost by nearly an order of magnitude. Based on this result, GPT-5.4-nano with high reasoning effort was selected for extraction across the full dataset. Our initial analysis suggests that MD system sizes and the number of independent runs have not grown substantially over the last two decades, whereas simulation duration shows a clearer, approximately exponential increase. The fitted trend suggests that maximum MD simulation duration doubled approximately every 2.2 years between 2005 and 2025. These results demonstrate the feasibility of AI-assisted meta-analysis for understanding historical trends in scientific computing workloads.