The characterization of an ionic liquid’s properties based on structural information is a longstanding goal of computational chemistry, which has received much focus from ab initio and molecular dynamics calculations. This work examines kernel ridge regression models built from an experimental dataset of 2212 ionic liquid melting points consisting of diverse ion types. Structural descriptors, which have been shown to predict quantum mechanical properties of small neutral molecules within chemical accuracy, benefit from the addition of first-principles data related to the target property (molecular orbital energy, charge density profile, and interaction energy based on the geometry of a single ion pair) when predicting the melting point of ionic liquids. Out of the two chosen structural descriptors, ECFP4 circular fingerprints and the Coulomb matrix, the addition of molecular orbital energies and all quantum mechanical data to each descriptor, respectively, increases the accuracy of surrogate models for melting point prediction compared to using the structural descriptors alone. The best model, based on ECFP4 and molecular orbital energies, predicts ionic liquid melting points with an average mean absolute error of 29 K and, unlike group contribution methods, which have achieved similar results, is applicable to any type of ionic liquid.

中文翻译：

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描述符选择在离子液体熔点预测的机器学习模型中的作用。

基于结构信息表征离子液体的性质是计算化学的一个长期目标，从头开始受到了很多关注和分子动力学计算。这项工作研究了由2212种离子液体熔点的实验数据集（由多种离子类型组成）建立的核岭回归模型。已经显示出结构描述符可预测化学精度范围内的中性小分子的量子力学性能，这得益于与目标特性（分子轨道能量，电荷密度分布和基于几何形状的相互作用能）有关的第一原理数据的添加当预测离子液体的熔点时）。从两个选择的结构描述符ECFP4圆形指纹和库仑矩阵中，分别向每个描述符添加分子轨道能和所有量子力学数据，与仅使用结构描述符相比，可以提高替代模型预测熔点的准确性。基于ECFP4和分子轨道能的最佳模型可预测离子液体的熔点，其平均平均绝对误差为29 K，并且与获得相似结果的基团贡献方法不同，该模型适用于任何类型的离子液体。