Department of Mathematics, University of South Carolina, Columbia, South Carolina 29208, United States.
Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States.
J Chem Theory Comput. 2022 May 10;18(5):3099-3110. doi: 10.1021/acs.jctc.1c01201. Epub 2022 Apr 11.
Cationic cobaltocenium derivatives are promising components of the anion exchange membranes because of their excellent thermal and alkaline stability under the operating conditions of a fuel cell. Here, we present an efficient modeling approach to assessing the chemical stability of substituted cobaltocenium CoCp, based on the computed electronic structure enhanced by machine learning techniques. Within the aqueous environment, the positive charge of the metal cation is balanced by the hydroxide anion through formation of the CoCpOH complexes, whose dissociation is studied within the implicit solvent employing the density functional theory. The data set of about 118 the CoCpOH complexes based on 42 substituent groups characterized by a range of electron-donating (ED) and electron-withdrawing (EW) properties is constructed and analyzed. Given 12 carefully chosen chemistry-informed descriptors of the complexes and relevant fragments, the stability of the complexes is found to strongly correlate with the energies of the highest occupied and lowest unoccupied molecular orbitals, modulated by a switching function of the Hirshfeld charge. The latter is used as a measure of the electron-withdrawing-donating character of the substituents. On the basis of this observation from the conventional regression analysis, two fully connected, feed-forward neural network (FNN) models with different unit structures, called the chemistry-informed (CINN) and the quadratic (QNN) neural networks, together with a support vector regression (SVR) model are developed. Both deep neural network models predict the bond dissociation energies of the cobaltocenium complexes with mean relative errors less than 10.56% and average absolute errors less than 1.63 kcal/mol, superior to the conventional regression and the SVR model prediction. The results show the potential of QNN to efficiently capture more complex relationships. The concept of incorporating the domain (chemical) knowledge/insight into the neural network structure paves the way to applications of machine learning techniques with small data sets, ultimately leading to better predictive models compared to the classical machine learning method SVR and conventional regression analysis.
阳离子钴卟啉衍生物由于在燃料电池的工作条件下具有优异的热稳定性和碱性稳定性，是阴离子交换膜的有前途的组成部分。在这里，我们提出了一种有效的建模方法，基于机器学习技术增强的计算电子结构来评估取代的钴卟啉 CoCp 的化学稳定性。在水相环境中，金属阳离子的正电荷通过形成 CoCpOH 配合物与氢氧根阴离子平衡，在隐式溶剂中使用密度泛函理论研究其离解。基于 42 种取代基，构建并分析了大约 118 种具有一系列供电子（ED）和吸电子（EW）性质的 CoCpOH 配合物的数据集。考虑到 12 种精心选择的配合物和相关片段的化学信息描述符，发现配合物的稳定性与最高占据和最低未占据分子轨道的能量强烈相关，由希夫尔德电荷的开关函数调制。后者用于衡量取代基的吸电子给电子性质。基于传统回归分析的这一观察结果，开发了两种具有不同单元结构的完全连接前馈神经网络（FNN）模型，称为化学信息（CINN）和二次（QNN）神经网络，以及支持向量回归（SVR）模型。这两种深度神经网络模型都以平均相对误差小于 10.56%和平均绝对误差小于 1.63 kcal/mol 的精度预测钴卟啉配合物的键离解能，优于传统回归和 SVR 模型的预测。结果表明，QNN 具有有效地捕捉更复杂关系的潜力。将领域（化学）知识/洞察力纳入神经网络结构的概念为具有小数据集的机器学习技术的应用铺平了道路，与经典机器学习方法 SVR 和传统回归分析相比，最终导致更好的预测模型。
