Alkali metals are widely used as industrial materials in products such as electrochemical cells because of their properties that make them suited to high temperatures. In this study, three computational approaches including gene expression programming (GEP), least squares support vector machine (LSSVM), and adaptive neuro fuzzy inference system (ANFIS) have been suggested to estimate density of different liquid alkali metals in extensive ranges of pressure and temperature. An experimental databank involving 595 experimental alkali metals’ densities has been gathered to prepare and test the models. The mathematical and visual comparisons of these models’ outputs and real density values are used to assess capacities of GEP, LSSVM, and ANFIS models in prediction of alkali metals’ density. The determined R-squared values for GEP, LSSVM, and ANFIS are 0.9999, 1, and 1, respectively. The MSE values are estimated to be 0.9184, 0.815, and 0.154 for GEP, ANFIS, and LSSVM, respectively. According to these results, these models can be suggested as simple and accurate ways for determining alkali metals’ properties. Results showed that LSSVM has the best performance in comparison with GEP and ANFIS. Moreover, the parametric analysis of input parameters is carried out to show the impact of them on alkali metals’ density. According to this analysis, the amount of lithium can be the most effective parameter on the mixture density.

中文翻译：

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用于在宽范围内估计液态碱金属密度的不断发展的机器学习方法

碱金属因其适合高温的特性而被广泛用作电化学电池等产品中的工业材料。在这项研究中，提出了三种计算方法，包括基因表达编程 (GEP)、最小二乘支持向量机 (LSSVM) 和自适应神经模糊推理系统 (ANFIS)，以估计不同液体碱金属在广泛的压力和压力范围内的密度。温度。已经收集了一个涉及 595 个实验碱金属密度的实验数据库来准备和测试模型。这些模型的输出和实际密度值的数学和视觉比较用于评估 GEP、LSSVM 和 ANFIS 模型在预测碱金属密度方面的能力。确定的 GEP、LSSVM 的 R 平方值，和 ANFIS 分别为 0.9999、1 和 1。GEP、ANFIS 和 LSSVM 的 MSE 值估计分别为 0.9184、0.815 和 0.154。根据这些结果，可以建议将这些模型作为确定碱金属性质的简单而准确的方法。结果表明，与 GEP 和 ANFIS 相比，LSSVM 的性能最好。此外，对输入参数进行了参数分析，以显示它们对碱金属密度的影响。根据该分析，锂的量可能是混合物密度的最有效参数。结果表明，与 GEP 和 ANFIS 相比，LSSVM 的性能最好。此外，对输入参数进行了参数分析，以显示它们对碱金属密度的影响。根据该分析，锂的量可能是混合物密度的最有效参数。结果表明，与 GEP 和 ANFIS 相比，LSSVM 的性能最好。此外，对输入参数进行了参数分析，以显示它们对碱金属密度的影响。根据该分析，锂的量可能是混合物密度的最有效参数。