npj:最低未占据轨道与缓蚀效率—有何秘密关系?
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由德国亥姆霍兹中心材料系统建模研究所的Roland C. Aydin教授和表面科学研究所的Christian Feiler教授领导的团队发现,稀疏特征选择方法可以帮助识别那些携带最有价值的分子描述符,以用于预测有机小分子对镁合金降解的缓蚀速率。除了经典的结构描述符,直接从DFT计算中导出的描述符也有一定作用,可作为备用方法。有趣的是,备用特征选择的方法揭示,化学高级模板搜索(CATS)的描述符具有巨大的应用潜力,如应用于人工智能驱动的药物发现。作者的研究表明,编码的药效基团特性也有助于描述有机小分子与金属离子(如Mg2+和Fe2+/3+)形成配合物的能力。在某些情况下,DFT衍生的描述符LUMO似乎作用重大,个体和群体特征选择的结果也证实了这一结论。对于小样本数据集,作者展示了自动编码器检测数据集中潜在的卓越能力。不过,作者开发的特殊处理方法很可能仅是大幅提高数据驱动的预测缓蚀速率的重要的第一步,只是为未来进一步研究开辟了一条充满希望的道路。
该文近期发表于npj Computational Materials 7: 193 (2021),英文标题与摘要如下,点击左下角“阅读原文”可以自由获取论文PDF。
Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models
Elisabeth J. Schiessler, Tim Würger, Sviatlana V. Lamaka, Robert H. Meißner, Christian J. Cyron, Mikhail L. Zheludkevich, Christian Feiler & Roland C. Aydin
The degradation behaviour of magnesium and its alloys can be tuned by small organic molecules. However, an automatic identification of effective organic additives within the vast chemical space of potential compounds needs sophisticated tools. Herein, we propose two systematic approaches of sparse feature selection for identifying molecular descriptors that are most relevant for the corrosion inhibition efficiency of chemical compounds. One is based on the classical statistical tool of analysis of variance, the other one based on random forests. We demonstrate how both can—when combined with deep neural networks—help to predict the corrosion inhibition efficiencies of chemical compounds for the magnesium alloy ZE41. In particular, we demonstrate that this framework outperforms predictions relying on a random selection of molecular descriptors. Finally, we point out how autoencoders could be used in the future to enable even more accurate automated predictions of corrosion inhibition efficiencies.
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