2026-08-06 アルゴンヌ国立研究所（ANL）
Argonne researchers developed a system that uses multiple AI agents to automate atomistic simulations from start to finish. An administrator agent orchestrates the overall workflow, assigning tasks to a series of specialist agents. (Image by Argonne National Laboratory.)
＜関連情報＞
- https://www.anl.gov/article/scientists-deploy-ai-agents-to-accelerate-discovery-of-new-materials
- https://pubs.rsc.org/dd/article/5/1/440/312882/Multi-agentic-AI-framework-for-end-to-end
- https://journals.aps.org/pr/abstract/10.1103/PhysRev.136.A405
エンドツーエンドの原子シミュレーションのためのマルチエージェントAIフレームワーク Multi-agentic AI framework for end-to-end atomistic simulations
Aikaterini Vriza;Uma Kornu;Aditya Koneru;Henry Chan;Subramanian K. R. S. Sankaranarayanan
Digital Discovery Published:09 December 2025
DOI:https://doi.org/10.1039/d5dd00435g
One of the main bottlenecks for the wide adoption of atomistic simulation pipelines for computational materials design is the high complexity of the workflows which many times requires the use of a diverse set of specialized toolkits and libraries. Here, we introduce a multi-agent artificial intelligence (AI) framework that autonomously performs end-to-end atomistic simulations, i.e. molecular dynamics (MD), with automated input and associated full suite of analyses, using large language models (LLMs) and multiple specialized AI agents. Our system orchestrates the entire simulation pipeline, from structure generation via Atomsk and interatomic potential discovery through automated web mining, to simulation setup and execution using LAMMPS on high-performance computing (HPC) platforms. Post-simulation, our agentic framework performs automated data analysis and visualization with popular analysis tools like OVITO and Phonopy. Each expert agent operates within a defined role, equipped with domain-specific functions and a shared memory context for coordination. Using a diverse set of representative elemental and alloy systems, we demonstrate the capability of our framework to execute a range of static and dynamic materials modeling tasks, including lattice parameter and cohesive energy estimation, elastic constants computation, phonon dispersion analysis, as well as perform MD simulations to determine dynamical properties that aid estimation of melting point. The results produced by the agents show strong agreement with those obtained by a human expert, highlighting the reliability of the agentic approach. By combining automation, reproducibility, and human-in-the-loop control, our framework lowers the barrier to the widespread adoption of scalable, AI-driven discovery tools in materials science.
液体アルゴン中の原子の運動における相関関係 Correlations in the Motion of Atoms in Liquid Argon
A. Rahman
Physical Review Journals Archive Published :19 October, 1964
DOI: https://doi.org/10.1103/PhysRev.136.A405
Abstract
A system of 864 particles interacting with a Lennard-Jones potential and obeying classical equations of motion has been studied on a digital computer (CDC 3600) to simulate molecular dynamics in liquid argon at 94.4°K and a density of 1.374 g cm−3. The pair-correlation function and the constant of self-diffusion are found to agree well with experiment; the latter is 15% lower than the experimental value. The spectrum of the velocity autocorrelation function shows a broad maximum in the frequency region ω=0.25(KBT/h). The shape of the Van Hove function Gs(r, t) attains a maximum departure from a Gaussian at about =3.0 ×10−12 sec and becomes a Gaussian again at about 10−11 sec. The Van Hove function Gd(r, t) has been compared with the convolution approximation of Vineyard, showing that this approximation gives a too rapid decay of Gd(r, t) with time. A delayed-convolution approximation has been suggested which gives a better fit with Gd(r, t); this delayed convolution makes Gd(r, t) decay as t4 at short times and as at long times.
