Department of Chemistry, Inorganic Chemistry Section, Jadavpur University, Kolkata 700032, India.
School of Biological Sciences, Indian Association for the Cultivation of Science, Kolkata 700 032, India.
Dalton Trans. 2022 Dec 20;52(1):97-108. doi: 10.1039/d2dt03289a.
We implemented both neural network and decision tree-based machine learning tools to analyse the anion-responsive behaviours of two heteroleptic Ru(II) complexes based on two tridentate ligands, 2,6-bis(benzimidazole-2-yl)pyridine (Hpbbzim) and substituted terpyridine ligands, tpy-Ar with Ar = 2-naphthyl and 9-anthryl groups. The secondary coordination sphere of the complexes is decorated with two imidazole NH moieties, benefitting from the anion sensing characteristics of the complexes previously reported by us. Considerable change in their absorption, emission as well as electrochemical and spectroelectrochemical responses occur in the presence of selected anions. Restoration of their initial states is made possible by acid and the process is reversible. We utilized their spectral, electrochemical and spectroelectrochemical responses upon the influence of anions and acid to mimic the operations of YES-NOT and set-reset flip-flop logic gates. We also implemented machine learning tools such as artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and decision tree (DT) regression to analyse and forecast the experimental data and can thus reduce the time and expenditure associated with the execution of comprehensive sensing experiments. The outcomes of the ANN, ANFIS and DT methods were also tallied with the experimental results. Among the three models, the outcomes derived from DT regression analysis turned out to be excellent with almost zero error. Thus, the applied machine learning based tools could be regarded as a prospective anion-responsive data model for the studied complexes.
我们实现了基于神经网络和决策树的机器学习工具，以分析两种基于两个三齿配体的异双核 Ru(II)配合物的阴离子响应行为，这两个三齿配体是 2,6-双（苯并咪唑-2-基）吡啶（Hpbbzim）和取代的三联吡啶配体 tpy-Ar，其中 Ar 为 2-萘基和 9-蒽基。配合物的次级配位球上装饰有两个咪唑 NH 部分，这得益于我们之前报道的配合物的阴离子传感特性。在选定的阴离子存在下，它们的吸收、发射以及电化学和光谱电化学响应会发生相当大的变化。通过酸可以恢复它们的初始状态，并且该过程是可逆的。我们利用它们在阴离子和酸影响下的光谱、电化学和光谱电化学响应来模拟 YES-NOT 和置位-复位触发器逻辑门的操作。我们还实施了机器学习工具，如人工神经网络（ANNs）、自适应神经模糊推理系统（ANFIS）和决策树（DT）回归，以分析和预测实验数据，从而减少执行全面传感实验相关的时间和支出。ANN、ANFIS 和 DT 方法的结果也与实验结果进行了对比。在这三种模型中，来自 DT 回归分析的结果表现出色，几乎没有误差。因此，所应用的基于机器学习的工具可以被视为研究配合物的有前途的阴离子响应数据模型。
