Multi-AI agent framework reveals the "oxide gatekeeper" in aluminum nanoparticle oxidation.
4 affiliations
- 1Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai 980-8577, Japan.
- 2School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China.
- 3Suzhou MatSource Technology Co., Ltd., Suzhou, Jiangsu 215000, China.
- 4Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai 980-8578, Japan.
Abstract
Aluminum nanoparticles (ANPs) are among the most energy-dense solid fuels, yet the atomic mechanisms governing their transition from passivated particles to highly reactive states remain elusive. This stems from a fundamental computational bottleneck: Ab initio methods offer quantum-level fidelity but are restricted to small spatiotemporal scales (<500 atoms, picoseconds), while empirical force fields lack the reactive fidelity required for complex combustion environments. Here, we bridge this gap by employing a human-audited, multiagent framework to make the development and validation of a task-specific machine learning potential (MLP) more traceable and auditable. The workflow coordinates active learning, model evaluation, validation evidence, and human expert review, while leaving scientific interpretation and model-use decisions under researcher control. This collaborative cycle ensures quantum mechanical accuracy while exhibiting near-linear scalability. Strikingly, our simulations reveal a temperature-regulated dual-mode oxidation mechanism: At moderate temperatures, the oxide shell acts as a dynamic "gatekeeper," regulating oxidation through a "breathing mode" of transient nanochannels; above a critical threshold, a "rupture mode" leads to shell failure and rapid combustion. Importantly, we resolve a decades-old controversy by demonstrating that aluminum cation outward diffusion, rather than oxygen transport, dominates mass transfer in studied regimes, with diffusion coefficients consistently exceeding those of oxygen by 2 to 3 orders of magnitude. These results provide an atomistic framework for interpreting ANP reactivity and illustrate how human-audited agentic workflows can make domain-specific reactive MLP development and deployment more reproducible.
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BibTeX
@article{lu2026multiai,
title = {{Multi-AI agent framework reveals the "oxide gatekeeper" in aluminum nanoparticle oxidation.}},
author = {Yiming Lu and Tingyu Lu and Di Zhang and Lili Ye and Hao Li},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = oct,
doi = {10.1073/pnas.2537780123},
url = {https://europepmc.org/article/MED/42879469},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multi-AI agent framework reveals the "oxide gatekeeper" in aluminum nanoparticle oxidation.
AU - Yiming Lu
AU - Tingyu Lu
AU - Di Zhang
AU - Lili Ye
AU - Hao Li
PY - 2026
DA - 2026/10/15
JO - Proceedings of the National Academy of Sciences of the United States of America
DO - 10.1073/pnas.2537780123
UR - https://europepmc.org/article/MED/42879469
AB - Aluminum nanoparticles (ANPs) are among the most energy-dense solid fuels, yet the atomic mechanisms governing their transition from passivated particles to highly reactive states remain elusive. This stems from a fundamental computational bottleneck: Ab initio methods offer quantum-level fidelity but are restricted to small spatiotemporal scales (<500 atoms, picoseconds), while empirical force fields lack the reactive fidelity required for complex combustion environments. Here, we bridge this gap by employing a human-audited, multiagent framework to make the development and validation of a task-specific machine learning potential (MLP) more traceable and auditable. The workflow coordinates active learning, model evaluation, validation evidence, and human expert review, while leaving scientific interpretation and model-use decisions under researcher control. This collaborative cycle ensures quantum mechanical accuracy while exhibiting near-linear scalability. Strikingly, our simulations reveal a temperature-regulated dual-mode oxidation mechanism: At moderate temperatures, the oxide shell acts as a dynamic "gatekeeper," regulating oxidation through a "breathing mode" of transient nanochannels; above a critical threshold, a "rupture mode" leads to shell failure and rapid combustion. Importantly, we resolve a decades-old controversy by demonstrating that aluminum cation outward diffusion, rather than oxygen transport, dominates mass transfer in studied regimes, with diffusion coefficients consistently exceeding those of oxygen by 2 to 3 orders of magnitude. These results provide an atomistic framework for interpreting ANP reactivity and illustrate how human-audited agentic workflows can make domain-specific reactive MLP development and deployment more reproducible.
SN - 0027-8424
SN - 1091-6490
ER -