AI 智能体自主优化数值算法：应用于原子模拟
Autonomous Optimization of Numerical Algorithms by an AI Agent: Application to Atomistic Simulation
AI 导读
arXiv 论文提出 AIOA 框架，让基于大语言模型的编码智能体在不可修改的验证器守护下，自主优化原子模拟的数值算法，并应用于广泛使用的原子模拟库 ASE。
正文 · 原文
Abstract:Coding agents built on large language models, run in automated improvement loops, can discover more efficient algorithms. We present AIOA, a framework that brings this approach to the numerical algorithms of atomistic simulation: an agent improves an algorithm against a pre-specified metric in a loop guarded by verifiers it cannot alter. Applied to ASE, a widely used atomistic simulation library, AIOA targets two facets of an algorithm: its implementation, rewritten to run faster with unchanged results, and the method itself, redesigned to converge with fewer force evaluations. Agents made six kernels 22-24 times faster on average in Python with NumPy and SciPy. On unseen problems, optimizers from the best configuration needed 3.1-3.4 times fewer force evaluations than L-BFGS for structure relaxation and 4.6-17 times fewer than standard climbing-image nudged elastic band calculations for reaction paths. The agents' optimizers also contain two algorithmic components we did not find in the literature.
| Comments: | 29 pages, 5 figures, 1 table; Supplementary Information included |
| Subjects: | Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13143 [physics.chem-ph] |
| (or arXiv:2611.13143v1 [physics.chem-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13143 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jeheon Woo [view email]
[v1]
Mon, 9 Nov 2026 19:23:39 UTC (131 KB)
来源：arXiv cs.AI · arxiv.org