Multi-AI Agent Framework Reveals the 'Oxide Gatekeeper' in Aluminum Nanoparticle Oxidation

📝 Abstract

Aluminum nanoparticles (ANPs) are among the most energy-dense solid fuels, yet the atomic mechanisms governing their transition from passivated particles to explosive reactants remain elusive. This stems from a fundamental computational bottleneck: ab initio methods offer quantum accuracy but are restricted to small spatiotemporal scales (< 500 atoms, picoseconds), while empirical force fields lack the reactive fidelity required for complex combustion environments. Herein, we bridge this gap by employing a “human-in-the-loop” closed-loop framework where self-auditing AI Agents validate the evolution of a machine learning potential (MLP). By acting as scientific sentinels that visualize hidden model artifacts for human decision-making, this collaborative cycle ensures quantum mechanical accuracy while exhibiting near-linear scalability to million-atom systems and accessing nanosecond timescales (energy RMSE: 1.2 meV/atom, force RMSE: 0.126 eV/Angstrom). 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” unleashes catastrophic shell failure and explosive combustion. Importantly, we resolve a decades-old controversy by demonstrating that aluminum cation outward diffusion, rather than oxygen transport, dominates mass transfer across all temperature regimes, with diffusion coefficients consistently exceeding those of oxygen by 2-3 orders of magnitude. These discoveries establish a unified atomic-scale framework for energetic nanomaterial design, enabling the precision engineering of ignition sensitivity and energy release rates through intelligent computational design.

💡 Analysis

Aluminum nanoparticles (ANPs) are among the most energy-dense solid fuels, yet the atomic mechanisms governing their transition from passivated particles to explosive reactants remain elusive. This stems from a fundamental computational bottleneck: ab initio methods offer quantum accuracy but are restricted to small spatiotemporal scales (< 500 atoms, picoseconds), while empirical force fields lack the reactive fidelity required for complex combustion environments. Herein, we bridge this gap by employing a “human-in-the-loop” closed-loop framework where self-auditing AI Agents validate the evolution of a machine learning potential (MLP). By acting as scientific sentinels that visualize hidden model artifacts for human decision-making, this collaborative cycle ensures quantum mechanical accuracy while exhibiting near-linear scalability to million-atom systems and accessing nanosecond timescales (energy RMSE: 1.2 meV/atom, force RMSE: 0.126 eV/Angstrom). 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” unleashes catastrophic shell failure and explosive combustion. Importantly, we resolve a decades-old controversy by demonstrating that aluminum cation outward diffusion, rather than oxygen transport, dominates mass transfer across all temperature regimes, with diffusion coefficients consistently exceeding those of oxygen by 2-3 orders of magnitude. These discoveries establish a unified atomic-scale framework for energetic nanomaterial design, enabling the precision engineering of ignition sensitivity and energy release rates through intelligent computational design.

📄 Content

1 Multi-AI Agent Framework Reveals the “Oxide Gatekeeper” in Aluminum Nanoparticle Oxidation
Yiming Lu1,2, Tingyu Lu1, Di Zhang1, Lili Ye2, Hao Li1 1 Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai 980-8577, Japan 2 School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China

• Corresponding author Email: yell@dlut.edu.cn (L. Ye) li.hao.b8@tohoku.ac.jp (H. Li)

Abstract Aluminum nanoparticles (ANPs) are among the most energy-dense solid fuels, yet the atomic mechanisms governing their transition from passivated particles to explosive reactants remain elusive. This stems from a fundamental computational bottleneck: ab initio methods offer quantum accuracy but are restricted to small spatiotemporal scales (< 500 atoms, picoseconds), while empirical force fields lack the reactive fidelity required for complex combustion environments. Herein, we bridge this gap by employing a “human-in-the-loop” closed-loop framework where self-auditing AI Agents validate the evolution of a machine learning potential (MLP). By acting as scientific sentinels that visualize hidden model artifacts for human decision-making, this collaborative cycle ensures quantum mechanical accuracy while exhibiting near-linear scalability to million-atom systems and accessing nanosecond timescales (energy RMSE: 1.2 meV/atom, force RMSE: 0.126 eV/Å). 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” unleashes catastrophic shell failure and explosive combustion. Importantly, we resolve a decades-old controversy by demonstrating that aluminum cation outward diffusion, rather than oxygen transport, dominates mass transfer across all temperature regimes, with diffusion coefficients consistently exceeding those of oxygen by 2-3 orders of magnitude. These discoveries establish a unified atomic-scale framework for energetic nanomaterial design, enabling the precision engineering of ignition sensitivity and energy release rates through intelligent computational design. Keywords: Aluminum Nanoparticles; AI Agents; Machine Learning Potential; Oxidation Mechanism

2

1. Introduction The quest for high-performance energy storage and propulsion systems has driven intense interest in aluminum nanoparticles (ANPs), which deliver exceptional energy densities of up to 31 kJ/g [1] and serve as critical components in advanced aerospace and defense technologies [2], [3], [4]. Despite decades of research, a fundamental mystery has persisted: how do these nanoscale powerhouses transition from benign, passivated particles at ambient conditions to explosive reactants at elevated temperatures? This dramatic behavioral shift spans an extraordinary range— from room-temperature oxide shell formation to violent high-temperature combustion involving complex gas-phase aluminum (Al) species. The underlying atomic-scale processes that control this transition have remained elusive, creating a critical knowledge gap that limits our ability to precisely engineer energy release rates and optimize combustion efficiency. Resolving this long- standing puzzle requires understanding the fundamental mass transport mechanisms that govern ANP reactivity across the complete temperature spectrum—a challenge that has defied conventional experimental and theoretical approaches due to the extreme spatiotemporal scales involved, from picosecond atomic motions to millisecond macroscopic energy release [8], [9], [10]. Accurate simulation of ANP combustion faces a fundamental scientific challenge: achieving large-scale spatiotemporal simulations while maintaining quantum mechanical accuracy. Current mainstream theoretical methods suffer from inherent limitations. Quantum mechanical approaches, exemplified by density functional theory (DFT), can precisely describe interatomic interactions and electronic structures but are computationally prohibitive, restricting simulations to hundreds of atoms and picosecond timescales—far from covering complete combustion evolution [11], [12]. Conversely, classical reactive force fields like reactive force field (ReaxFF) can handle larger scales (thousands of atoms, nanosecond timescales) [7], [13] but rely on empirical parameter fitting. This limitation often confines studies to phenomenological observations rather than establishing rigorous physical laws. A critical question arises: is the previously observed dominance of Al outward diffusion a genuine physical phenomenon, or merely an artifact of parameterization? To transit from qualitative simulation to quantitative confirmation, a method combining quantum precision with macroscopic scalability is required. Furthermore, ReaxFF often struggles to accurat

This content is AI-processed based on ArXiv data.