当前位置：
X-MOL 学术
›
Cryst. Growth Des.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Relationships between Changes in Guest Ion Properties and in the Host Framework Topology in Ionic Coordination Polymers
Crystal Growth & Design ( IF 3.2 ) Pub Date : 2021-07-12 , DOI: 10.1021/acs.cgd.1c00405 Pavel N. Zolotarev 1
Crystal Growth & Design ( IF 3.2 ) Pub Date : 2021-07-12 , DOI: 10.1021/acs.cgd.1c00405 Pavel N. Zolotarev 1
Affiliation
|
In order to identify possible general relationships between changes in the framework topology and changes in the extra framework ion properties, a set consisting of 2202 crystal structures of ionic coordination polymers was extracted from the Cambridge Structural Database. Changes in ion properties served as independent variables for several machine learning models trained to predict the changes in framework dimensionality, topological density, and average ring size of the framework’s tiling. The trained classifiers showed acceptable predictive performance with F1 score in the range 0.4 ÷ 0.6 and were subjected to the validation tests, which confirmed that they fit the data significantly better than by chance. Subsequent feature importance analysis of the classifiers revealed a set of the ion properties being important for prediction of the changes in corresponding framework characteristics in the extracted set of crystal structures. It is shown that in general changes in molecular surface area and molecular flexibility of the guest ions are essential for predicting changes in selected topological characteristics of a framework. Case studies were conducted for several sets of crystal structures with frameworks that are observed to host significant variety of counter ions. The decision tree classifiers allowed us to discover the ion properties determining topological characteristics in particular frameworks.
中文翻译：
离子配位聚合物中客体离子特性变化与主体框架拓扑结构变化之间的关系
为了确定骨架拓扑变化与额外骨架离子性质变化之间可能的一般关系，从剑桥结构数据库中提取了由 2202 个离子配位聚合物晶体结构组成的集合。离子特性的变化作为几个机器学习模型的自变量，这些模型经过训练以预测框架维数、拓扑密度和框架平铺的平均环大小的变化。经过训练的分类器显示出可接受的预测性能，F 1得分在 0.4 ÷ 0.6 范围内，并进行了验证测试，这证实它们比偶然更适合数据。随后对分类器的特征重要性分析揭示了一组离子特性对于预测提取的一组晶体结构中相应骨架特征的变化很重要。结果表明，一般来说，客体离子的分子表面积和分子柔韧性的变化对于预测框架的选定拓扑特征的变化是必不可少的。对几组具有框架的晶体结构进行了案例研究，观察到这些结构承载着大量的反离子。决策树分类器使我们能够发现确定特定框架中拓扑特征的离子特性。
更新日期：2021-09-01
中文翻译：
离子配位聚合物中客体离子特性变化与主体框架拓扑结构变化之间的关系
为了确定骨架拓扑变化与额外骨架离子性质变化之间可能的一般关系，从剑桥结构数据库中提取了由 2202 个离子配位聚合物晶体结构组成的集合。离子特性的变化作为几个机器学习模型的自变量，这些模型经过训练以预测框架维数、拓扑密度和框架平铺的平均环大小的变化。经过训练的分类器显示出可接受的预测性能，F 1得分在 0.4 ÷ 0.6 范围内，并进行了验证测试，这证实它们比偶然更适合数据。随后对分类器的特征重要性分析揭示了一组离子特性对于预测提取的一组晶体结构中相应骨架特征的变化很重要。结果表明，一般来说，客体离子的分子表面积和分子柔韧性的变化对于预测框架的选定拓扑特征的变化是必不可少的。对几组具有框架的晶体结构进行了案例研究，观察到这些结构承载着大量的反离子。决策树分类器使我们能够发现确定特定框架中拓扑特征的离子特性。
京公网安备 11010802027423号