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首页> 外文期刊>Applied Surface Science >Prediction of corrosion inhibition efficiency of pyridines and quinolines on an iron surface using machine learning-powered quantitative structure- property relationships
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Prediction of corrosion inhibition efficiency of pyridines and quinolines on an iron surface using machine learning-powered quantitative structure- property relationships

机译：采用机械学习动力的定量结构 - 财产关系预测吡啶和喹啉在铁表面上吡啶和喹啉的腐蚀抑制效率

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摘要

Linear and non-linear quantitative structure-property relationship (QSPR) models were developed to predict corrosion inhibition efficiency for a series of 41 pyridine and quinoline N-heterocycles. Out of 20 physicochemical and quantum chemical variables related to the surface adsorption behaviour of the inhibitors, consensus models were constructed using the genetic algorithm-partial least squares (GA-PLS) and genetic algorithm-artificial neural network (GA-ANN) methods. The consensus GA-PLS model comprised of eight variables (exponential of the calculated adsorption energy, LUMO, van der Waals surface area and volume, polarizability, electron affinity, electrophilicity, electron donor capacity) exhibited an %RMSECV of 16.5% and mean %RMSE of 14.9%. Such a model moderately captured the complex relationships between inhibition efficiency and the quantum chemical variables. The consensus GA-ANN model comprised of nine input variables (exponential of the calculated adsorption energy, HOMO, LUMO, HOMO-LUMO Gap, electronegativity, softness, electrophilicity, electron donor capacity and N atomic charge) exhibited an %RMSECV of 16.7 +/- 2.3% and mean RMSE (training/testing/validation) of 8.8%, performing better than its linear counterpart in terms of predictive ability. Both models revealed the importance of adsorption to the metal surface, and electronic parameters quantifying electron acceptance/donation to/from the iron surface, suggesting key corrosion inhibition design principles.
机译：开发了线性和非线性定量结构性质关系（QSPR）模型以预测一系列41吡啶和喹啉N-杂环的腐蚀抑制效率。在与抑制剂的表面吸附行为相关的20个物理化学和量子化学变量中，使用遗传算法 - 局部最小二乘（GA-PL）和遗传算法 - 人工神经网络（GA-ANN）方法构建共识模型。由八个变量组成的共识GA-PLS模型（计算出的吸附能量，LUMO，VAN DAR WALS表面积和体积，极化性，电子亲和性，电泳，电子供体能力）表现出16.5％的％RMSECV和平均值RMSE 14.9％。这种模型适度地捕获了抑制效率与量子化学变量之间的复杂关系。由九个输入变量（计算的吸附能量，HONO，LUMO，HOMO-LUMO间隙，电负性，柔软性，亲电子性，电子供体和N原子充电的指数组成的共识GA-ANN模型表现出16.7 + /的％RMSECV / - 2.3％和平均RMSE（培训/测试/验证）为8.8％，比预测能力方面的线性对应物更好。两种型号揭示了吸附对金属表面的重要性，以及量化电子接受/捐赠给/从铁表面的电子参数，表明关键腐蚀抑制设计原理。

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