Abstract Comprehensive experimental investigation and accurate predictive models are required to understand the dynamics in Ionic liquid (IL) properties. Examples of these predictive models are empirical correlations, Quantitative structure–activity relationship (QSPR) and machine learning (ML). In this study, we reported the application of various ML models for predicting thermo-physical properties of ILs. Our study showed that these ML models could be categorized into conventional and hybrid models. These conventional models include artificial neural networks (ANN), least square support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS). Meanwhile, the hybrid models consist of random forest, gradient boosting, and group method of data handling. We provided an overview of these ML models and optimization methods such as genetic algorithm, particle swarm algorithm, and coupled simulated annealing (CSA) algorithm, and their applications in IL studies. We observed that ANN, LSSVM and ANFIS represent the three most frequently used ML approaches in predicting the various properties of ILs among the models discussed. The investigation revealed that the ANN approach is most widely used, while the studies involving the solubility of gases (H2S and CO2) represent the most common problems related to ML application in IL studies. However, the combination of conventional ML and optimization algorithms such as LSSVM-CSA gives better accuracy compared to ANN in most applications. It is noteworthy that system parameters (temperature and pressure) and critical properties (critical temperature and critical pressure) are the key thermo-physical that depicts the phase behavior of any ILs. Finally, to generalize MLs methods to certain ILs based on similarity in cations and anions, it is important to represent the molecular descriptions of the liquid as one of the property predictors.

中文翻译：

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基于人工智能的预测方法在离子液体研究中的应用：综述

摘要 了解离子液体 (IL) 特性的动力学需要全面的实验研究和准确的预测模型。这些预测模型的例子有经验相关性、定量结构-活性关系 (QSPR) 和机器学习 (ML)。在这项研究中，我们报告了各种 ML 模型在预测 IL 的热物理特性方面的应用。我们的研究表明，这些 ML 模型可以分为传统模型和混合模型。这些传统模型包括人工神经网络 (ANN)、最小二乘支持向量机 (LSSVM) 和自适应神经模糊推理系统 (ANFIS)。同时，混合模型由随机森林、梯度提升和数据处理的分组方法组成。我们概述了这些 ML 模型和优化方法，例如遗传算法、粒子群算法和耦合模拟退火 (CSA) 算法，以及它们在 IL 研究中的应用。我们观察到 ANN、LSSVM 和 ANFIS 代表了三种最常用的 ML 方法，用于预测所讨论模型中 IL 的各种属性。调查显示，ANN 方法使用最广泛，而涉及气体（H2S 和 CO2）溶解度的研究代表了与 IL 研究中 ML 应用相关的最常见问题。然而，在大多数应用中，与 ANN 相比，传统 ML 和优化算法（如 LSSVM-CSA）的组合提供了更好的准确性。值得注意的是，系统参数（温度和压力）和临界特性（临界温度和临界压力）是描述任何 IL 相行为的关键热物理。最后，为了根据阳离子和阴离子的相似性将 MLs 方法推广到某些 ILs，重要的是将液体的分子描述表示为特性预测因子之一。