Machine learning for pyrimidine corrosion inhibitor small dataset

IF 1.6 4区 化学 Q4 CHEMISTRY, PHYSICAL
Wise Herowati, Wahyu Aji Eko Prabowo, Muhamad Akrom, Noor Ageng Setiyanto, Achmad Wahid Kurniawan, Novianto Nur Hidayat, Totok Sutojo, Supriadi Rustad
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引用次数: 0

Abstract

Machine learning (ML) approaches have been developed to predict materials’ corrosion inhibition efficiency, particularly pyrimidine compounds. Notably, the virtual sample generation (VSG) technique enhances prediction accuracy, a novel approach for handling small datasets in this context. The random forest model, the best-performing nonlinear algorithm, showed substantial accuracy improvement based on the increase in R2 value from 0.05 to 0.99 and the decrease in RMSE value from 5.60 to 0.42, after applying VSG. These results underscore the efficacy of the VSG technique in boosting the predictive performance of ML models, particularly in scenarios constrained by limited data availability.

Abstract Image

嘧啶腐蚀抑制剂小型数据集的机器学习
人们开发了机器学习(ML)方法来预测材料的缓蚀效率,尤其是嘧啶化合物。值得注意的是,虚拟样本生成(VSG)技术提高了预测的准确性,是在这种情况下处理小数据集的一种新方法。随机森林模型是性能最好的非线性算法,在应用虚拟样本生成技术后,R2 值从 0.05 提高到 0.99,RMSE 值从 5.60 降低到 0.42,从而大幅提高了预测精度。这些结果凸显了 VSG 技术在提高 ML 模型预测性能方面的功效,尤其是在受限于数据可用性的情况下。
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来源期刊
Theoretical Chemistry Accounts
Theoretical Chemistry Accounts 化学-物理化学
CiteScore
3.40
自引率
0.00%
发文量
74
审稿时长
3.8 months
期刊介绍: TCA publishes papers in all fields of theoretical chemistry, computational chemistry, and modeling. Fundamental studies as well as applications are included in the scope. In many cases, theorists and computational chemists have special concerns which reach either across the vertical borders of the special disciplines in chemistry or else across the horizontal borders of structure, spectra, synthesis, and dynamics. TCA is especially interested in papers that impact upon multiple chemical disciplines.
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