Reviewed by Lexie CornerMar 4 2025
Researchers at Lawrence Livermore National Laboratory (LLNL) are addressing corrosion by developing predictive models to forecast failures and guide the design of more durable materials from the start. The study was published in Nature Communications.
Atomic force microscope image of porous nickel oxide formed during the dissolution-reprecipitation process. Image Credit: Lawrence Livermore National Laboratory
Corrosion costs trillions of dollars worldwide, and the cost of failing materials can account for as much as 3 % of the US GDP.
Our current knowledge of corrosion is based on historical data from well-known and well-characterized metal compositions and processing. As soon as you alter the composition at all or alter the way the materials are processed, all bets are off.
Brandon Wood, Study Author and Scientist, Lawrence Livermore National Laboratory
Using an advanced kinetic modeling technique, the team efficiently simulated corrosion processes with both speed and precision, revealing the impact of operating conditions and material composition.
The researchers focused on the natural protective oxide film that forms on metals. This film is crucial for protecting the metal, as corrosion can occur if the film dissolves, cracks, or becomes susceptible to attack.
Former LLNL postdoctoral researcher Penghao Xiao, now at Dalhousie University, developed multi-scale simulations to track the growth, dissolution, and compositional changes of oxides over time in response to environmental factors like pH and voltage. Due to the complexity of applying this approach to every material and environment, the team trained a machine learning-inspired model to predict the onset and causes of corrosion.
Using this framework, the authors investigated three voltage regimes. While the intermediate regime was not fully understood, the high and low-voltage environments are well-researched and documented.
Until now, no one was really able to explain what exactly was going on in that regime. We showed there is competition between two processes: dissolution and reprecipitation. When molecules leave the surface, mix and redeposit, the oxide looks completely different.
Chris Orme, Scientist and Experimental Lead, Lawrence Livermore National Laboratory
Although some systems, such as batteries, may directly apply voltage, the same phenomenon is surprisingly common in other settings as well.
Putting certain metals close to one another creates a sort of microbattery that can drive corrosion. This has been a problem in building ships and bridges, for instance. Our model can in principle account for such effects, while also being flexible enough to consider the interplay between the corrosive environment and the base metal composition.
Brandon Wood, Study Author and Scientist, Lawrence Livermore National Laboratory
This is just one example of how the model could be useful. By developing predictive tools and improving the understanding of corrosion, this research can help design longer-lasting materials.
The research was part of an LLNL strategic initiative focused on predicting material failures. Funding came from the Laboratory Directed Research and Development program. Other authors from LLNL include Christine A. Orme, S. Roger Qiu, Tuan Anh Pham, Seongkoo Cho, and Michael Bagge-Hansen. Penghao Xiao, a former postdoctoral researcher at LLNL, led the simulations.
Journal Reference:
Xiao, P., et al. (2025) Atomic-scale understanding of oxide growth and dissolution kinetics of Ni-Cr alloys. Nature Communications. doi.org/10.1038/s41467-024-54627-x