DOE OSTI · 3001623
Autonomous alloy composition optimization using molecular dynamics guided by a large language model
Abstract
Here, we present an autonomous materials discovery framework that couples a large language model (LLM) with molecular dynamics (MD) simulations to optimize Fe–Cr–Mn alloy compositions for tensile strength. Starting from six distinct compositions, the LLM operated as an intelligent agent, iteratively proposing changes based on prior simulation results and constraints. Over 50 iterations per case, the LLM adaptively explored the composition space, identifying high-strength regions, not easily accessible by conventional methods. The highest strength, 18.7 GPa, was achieved with Fe 71 Cr 25 Mn 4 composition, identified from a Fe 75 Cr 20 Mn 5 starting point. The LLM autonomously adjusted its strategy in real time, demonstrating closed-loop decision-making using commodity hardware. This approach showcases the potential of LLMs as scientific co-pilots, capable of accelerating materials discovery and generalizable to other domains like biology and drug design.
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Roy, Ankit [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000334519217), Devanathan, Ram [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000181254237), Nartu, Mohan Sai Kiran Kumar Yadav [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000341655073), Joshi, Vineet V. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000176009317). 2025-10-17. Autonomous alloy composition optimization using molecular dynamics guided by a large language model. https://doi.org/10.1016/j.scriptamat.2025.117048
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