期刊
CORROSION SCIENCE
卷 163, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.corsci.2019.108245
关键词
Magnesium; Corrosion modulators; Density functional theory; QSPR
资金
- HZG MMDi IDEA project
- China Scholarship Council [201607040051]
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [192346071 - SFB 986]
The vast number of small molecules with potentially useful dissolution modulating properties (inhibitors or accelerators) renders currently used experimental discovery methods time- and resource-consuming. Fortunately, emerging computer-assisted methods can explore large areas of chemical space with less effort. Here we show how density functional theory calculations and machine learning methods can work synergistically to generate robust and predictive models that recapitulate experimentally-derived corrosion inhibition efficiencies of small organic compounds for pure magnesium. We further validate our methods by predicting a priori the corrosion modulation properties of seven hitherto untested small molecules and confirm the prediction in subsequent experiments.
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