In a study published in Frontiers in Batteries and Electrochemistry, Yasmin Shabeer, Seyed Saeed Madani, Satyam Panchal, and Michael Fowler systematically investigated the corrosion behavior of a commercial Al-Mg-Si alloy in sodium hydroxide (NaOH) and potassium hydroxide (KOH) electrolytes spanning concentrations from 1 to 10 molar. The team combined three complementary electrochemical techniques—open-circuit voltage monitoring, potentiodynamic polarization, and electrochemical impedance spectroscopy—with X-ray diffraction to assess structural stability. They then trained an artificial neural network on the resulting dataset and optimized it using two genetic algorithm variants, achieving coefficients of determination exceeding 0.999 for both corrosion potential and corrosion current density.

The experimental campaign revealed two distinct corrosion regimes. At low-to-moderate alkalinity, anodic activation dominates: the corrosion potential shifts progressively negative and the corrosion current density climbs as hydroxide concentration increases, reflecting accelerated chemical dissolution of the native Al2O3/AlOOH surface film. At high concentrations, the behavior transitions to transport-limited kinetics, where the accumulation of soluble aluminate species, increased electrolyte viscosity, and hydrogen bubble coverage of 20 to 50 percent of the surface physically throttle the reaction rate. This shift from charge-transfer control to mass-transport control was confirmed by the weak coupling between corrosion potential and current density observed in the high-molarity polarization data.

Perhaps the most striking finding is that the two electrolytes behave very differently despite supplying identical hydroxide activity at the same molarity. KOH consistently produced more negative corrosion potentials and higher corrosion current densities than NaOH at equivalent concentrations. The researchers attribute this to cation-specific interfacial effects: potassium ions have a larger ionic radius, lower hydration energy, and higher mobility than sodium ions, facilitating faster charge transport, stronger double-layer perturbation, and more aggressive hydrogen evolution. In practical terms, this means that electrolyte identity—not just hydroxide concentration—governs corrosion kinetics in alkaline aluminum systems.

Impedance spectroscopy provided mechanistic confirmation of these trends. The charge-transfer resistance decreased markedly with increasing hydroxide concentration in both electrolytes, consistent with progressive activation of anodic dissolution. At equivalent molarities, NaOH consistently exhibited higher charge-transfer resistance than KOH, corroborating the milder corrosion environment inferred from polarization measurements. The constant phase element exponent remained below unity across all conditions, confirming a heterogeneous and dynamically evolving interface shaped by continuous oxide dissolution, aluminate formation, and gas evolution.

X-ray diffraction added a crucial structural dimension to the analysis. The diffraction patterns were dominated by the characteristic reflections of face-centered cubic aluminum, with invariant peak positions across all concentrations and both electrolytes. No crystalline aluminum hydroxides, oxyhydroxides, or aluminate phases were detected under any condition, indicating that corrosion products are predominantly amorphous, nanocrystalline, or highly hydrated. This finding validates the electrochemical evidence for continuous surface dissolution with transient, non-protective films rather than bulk phase transformation.

On the modeling side, the team conducted a comprehensive grid search of 351 artificial neural network configurations, varying network depth, neuron allocation, learning rate, and Adam optimizer parameters. The best-performing model used a three-layer architecture with 20, 25, and 25 neurons, a learning rate of 0.001, and a beta-1 value of 0.92. This configuration achieved R-squared values of 0.999888 for corrosion potential and 0.999945 for corrosion current density, representing an almost perfect mapping between experimental variables and corrosion indicators. Notably, 76 percent of all tested configurations achieved mean validation R-squared values exceeding 0.97, indicating that the underlying electrochemical relationships are highly learnable yet sensitive to architectural choices.

The researchers then applied two fundamentally different optimization strategies to the trained network. A scalar genetic algorithm, which combined the two corrosion objectives into a single weighted fitness function, and a non-dominated sorting genetic algorithm (NSGA-II), which preserved the independence of both objectives and resolved the complete Pareto trade-off front. Remarkably, both approaches converged on the same optimal condition: approximately 1.35 molar KOH, with a predicted corrosion potential of −1.6199 volts and a corrosion current density of 0.0876 A cm−2. This knee-point solution represents the least-compromise operating condition, where further reductions in corrosion current would require a disproportionate loss in corrosion potential stability.

The agreement between the two optimization formulations provides strong validation of the framework. The Pareto front revealed a smooth, continuous trade-off between corrosion thermodynamics and kinetics, with pronounced curvature at low current densities where small increases in corrosion rate yield diminishing gains in potential stability. This nonlinear structure reflects the fundamental electrochemical coupling between anodic dissolution kinetics and corrosion thermodynamics in alkaline aluminum systems, and it underscores why empirical trial-and-error electrolyte selection often fails to identify truly optimal conditions.

The implications extend beyond aluminum-air batteries. The framework demonstrates that integrating systematic electrochemical experimentation with neural network-assisted evolutionary optimization enables rapid, quantitative evaluation of corrosion behavior without exhaustive testing. The researchers suggest that this approach could support the development of digital-twin platforms for the design, optimization, and control of next-generation aluminum-based electrochemical energy systems, where parasitic corrosion losses must be minimized without sacrificing electrochemical stability. As the global transition toward cleaner energy accelerates and demand for alternative battery chemistries grows, such corrosion-aware electrolyte optimization may prove essential for unlocking the full potential of aluminum-based energy storage.

Subject of Research: Machine learning prediction of aluminum anode corrosion kinetics in alkaline electrolytes for aluminum-air batteries

Article Title: Artificial neural network-genetic algorithm prediction of aluminum anode corrosion kinetics in high-concentration NaOH and KOH

Article References: Artificial neural network-genetic algorithm prediction of aluminum anode corrosion kinetics in high-concentration NaOH and KOH. (n.d.). https://doi.org/10.3389/fbael.2026.1815822

Image Credits: AI Generated

DOI: 10.3389/fbael.2026.1815822

Keywords: aluminum-air batteries, corrosion, artificial neural networks, genetic algorithm, NSGA-II, alkaline electrolytes, KOH, NaOH, Al-Mg-Si alloy, machine learning, electrochemistry, energy storage