Busch, MatthiasMatthiasBuschTacke, MariusMariusTackeLamaka, Sviatlana V.Sviatlana V.LamakaZheludkevich, Mikhail L.Mikhail L.ZheludkevichLinka, KevinKevinLinkaCyron, Christian J.Christian J.CyronFeiler, ChristianChristianFeilerAydin, RolandRolandAydin2025-07-162025-07-162025-10-01Corrosion Science 255: 113080 (2025)https://hdl.handle.net/11420/56218Large language models (LLMs), such as GPT-4o, have shown promise in solving everyday tasks and addressing fundamental scientific challenges by leveraging extensive pre-trained knowledge. In this study, we investigate their potential to predict the efficiency of various organic compounds in inhibiting the corrosion of the magnesium alloy ZE41. Traditional approaches, such as Multilayer Perceptrons (MLPs), rely on non-contextual data, often necessitating large datasets and substantial effort per sample to achieve accurate predictions. These methods particularly struggle with small datasets as their training data and domain of applicability is limited to a small area of the available chemical space. LLMs can contextualize and interpret limited data points by drawing on their vast knowledge, including the chemical properties of molecules and their influence on corrosion processes in other materials (e.g. iron and aluminium). By prompting the model with a small dataset, LLMs can provide meaningful predictions without the need for extensive training. Our study demonstrates that LLMs can predict corrosion inhibition outcomes and outperform classical approaches, such as MLPs, having access to the identical number of training samples.en0010-938X2025Elsevierhttps://creativecommons.org/licenses/by/4.0/Corrosion | Large language model | Machine learning | Magnesium | Material science | PredictionTechnology::620: Engineering::620.1: Engineering Mechanics and Materials ScienceComputer Science, Information and General Works::004: Computer SciencesNatural Sciences and Mathematics::541: Physical; TheoreticalJournal Articlehttps://doi.org/10.15480/882.1538810.1016/j.corsci.2025.11308010.15480/882.15388Journal Article