Kanahashi Kaito, Urushihara Makoto, Yamaguchi Kenji
Innovation Center, Mitsubishi Materials Corporation, 1002-14 Mukohyama, Naka, Ibaraki, 311-0102, Japan.
Department of Applied Physics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan.
Sci Rep. 2022 Jul 25;12(1):11159. doi: 10.1038/s41598-022-15300-9.
The stability constants of metal(M)-ligand(L) complexes are industrially important because they affect the quality of the plating film and the efficiency of metal separation. Thus, it is desirable to develop an effective screening method for promising ligands. Although there have been several machine-learning approaches for predicting stability constants, most of them focus only on the first overall stability constant of M-L complexes, and the variety of cations is also limited to less than 20. In this study, two Gaussian process regression models are developed to predict the first overall stability constant and the n-th (n > 1) overall stability constants. Furthermore, the feature relevance is quantitatively evaluated via sensitivity analysis. As a result, the electronegativities of both metal and ligand are found to be the most important factor for predicting the first overall stability constant. Interestingly, the predicted value of the first overall stability constant shows the highest correlation with the n-th overall stability constant of the corresponding M-L pair. Finally, the number of features is optimized using validation data where the ligands are not included in the training data, which indicates high generalizability. This study provides valuable insights and may help accelerate molecular screening and design for various applications.
金属（M）-配体（L）配合物的稳定常数在工业上具有重要意义，因为它们会影响镀膜的质量和金属分离的效率。因此，开发一种针对有前景配体的有效筛选方法是很有必要的。尽管已经有几种用于预测稳定常数的机器学习方法，但其中大多数仅关注M-L配合物的第一级总稳定常数，并且阳离子的种类也限制在20种以内。在本研究中，开发了两个高斯过程回归模型来预测第一级总稳定常数和第n级（n>1）总稳定常数。此外，通过敏感性分析对特征相关性进行了定量评估。结果发现，金属和配体的电负性是预测第一级总稳定常数的最重要因素。有趣的是，第一级总稳定常数的预测值与相应M-L对的第n级总稳定常数显示出最高的相关性。最后，使用验证数据对特征数量进行了优化，其中配体不包含在训练数据中，这表明具有很高的通用性。本研究提供了有价值的见解，并可能有助于加速各种应用的分子筛选和设计。
