In silico screening of modulators of magnesium dissolution

被引:48
作者
Feiler, Christian [1 ]
Mei, Di [1 ]
Vaghefinazari, Bahram [1 ]
Wuerger, Tim [1 ,2 ]
Meissner, Robert H. [1 ,2 ]
Luthringer-Feyerabend, Berengere J. C. [1 ,6 ]
Winkler, David A. [3 ,4 ,5 ]
Zheludkevich, Mikhail L. [1 ,6 ]
Lamaka, Sviatlana V. [1 ]
机构
[1] Helmholtz Zentrum Geesthacht, Magnesium Innovat Ctr MagiC, Inst Mat Res, Geesthacht, Germany
[2] Hamburg Univ Technol, Inst Polymer & Composites, Hamburg, Germany
[3] La Trobe Univ, La Trobe Inst Mol Sci, Kingsbury Dr, Bundoora, Vic, Australia
[4] CSIRO Mfg, Clayton, Vic, Australia
[5] Monash Univ, Monash Inst Pharmaceut Sci, Parkville, Vic, Australia
[6] Univ Kiel, Fac Engn, Inst Mat Sci, Kiel, Germany
关键词
Magnesium; Corrosion modulators; Density functional theory; QSPR; CORROSION-INHIBITORS; AIR BATTERIES; IRON; PREDICTION; ALLOYS; OPTIMIZATION; SHRINKAGE;
D O I
10.1016/j.corsci.2019.108245
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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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页数:8
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