Data driven accelerated design of high-efficiency corrosion inhibitor for magnetic refrigeration materials

Water-based heat exchange fluid causes severe corrosion of magnetic refrigeration materials. Here we successfully predict a high-efficiency corrosion inhibitor for magnetic refrigeration by using machine learning trained on a small database. The inhibitor has a significant corrosion inhibition effect, e.g., the La(Fe, Si)13 alloy maintains perfect surface after 60 days of immersion, whereas Na2MoO4 and Na2HPO4 from an inhibitor form the protective film to prevent corrosion. In comparison with the traditional trial-and-error method, the machine learning method could reduce data collection by approximately 26 times, while the prediction accuracy can be improved by approximately 2–3 orders of magnitude.