This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Building an accurate model of how atoms interact is a complicated process. Researchers must choose new calculations, test successive models and track the mechanisms behind each interaction.
But a newly developed AI agent called MLP Copilot has made this process much easier by bridging computational tools and scientific evidence with human supervision. "Just like autopilot makes life a lot easier for pilots, our general-purpose framework is designed to make materials science more efficient, without disregarding the need for actual humans to be at the reins," says Hao Li, distinguished professor at the Advanced Institute for Materials Research (WPI-AIMR), Tohoku University. MLP Copilot learns atomic energies and forces from quantum calculations, and the resulting models can go on to make larger simulations feasible.
The framework's coordination methods can be reused across materials systems, with calculations, training data and validation tailored to each application. The study was published in the Proceedings of the National Academy of Sciences on Oct. 15, 2026. To demonstrate the framework, the team investigated aluminum nanoparticle oxidation.
These particles store substantial chemical energy, but a surface oxide shell can limit their reaction with oxygen. Understanding how that shell changes could guide future efforts to control oxidation and energy release. Across approximately 80 active-learning iterations, the researchers assembled 88,833 atomic configurations.
Uncertain configurations guided additional quantum calculations and model training. Comparisons with reference results, thermodynamic properties and independent cluster tests helped assess the model before its use in oxidation simulations. The simulations revealed two behaviors of particles with an existing oxide shell.
At moderate temperatures within the study, temporary nanoscale pathways allowed oxidation to continue while limiting material transport—a "breathing mode." At higher temperatures, shell rupture exposed the aluminum core to oxygen and accelerated oxidation. Analyses supported outward aluminum transport as the dominant pathway under the conditions studied. Throughout the workflow, AI agents organized project knowledge, retrieved evidence and supported validation planning.
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