High specific heat molten-salt is essential for sensible heat thermal energy storage. Current scientific researches focus on Molten-salt nanofluid as a potential solution. However, the causality between system parameters introduced in nanofluid preparation and specific heat enhancement is not clearly understood. Since difficulties are associated with identifying the explicit relations due to complex molecular interactions between molten-salt and nanoparticles, we inquired whether there is a common pattern/clusters in the nanofluid samples reported in earlier studies. The data-driven correlations among samples are explored by employing unsupervised machine learning methods: Hierarchical cluster analysis (HCA) and Principal component analysis (PCA). Three principal components, capturing 81.3% variation of the entire dataset, revealed that the descending order of contribution of the system parameters in the specific heat enhancement percent is concentration, temperature, density ratio, and nanoparticle size. The multivariate clusters emerging from HCA showed the interdependency of density ratio on the temperature, which significantly affects nanofluid's stability at higher concentration, causing a decrease in specific heat enhanced percent. Furthermore, the variation in nanoparticle size was found to have a negligible effect on specific heat enhancement.

高比热熔盐对于显热热能储存是必不可少的。目前的科学研究集中在熔盐纳米流体作为一种潜在的解决方案。然而，纳米流体制备中引入的系统参数与比热增强之间的因果关系尚不清楚。由于熔盐和纳米粒子之间复杂的分子相互作用导致难以确定明确的关系，因此我们询问早期研究报告的纳米流体样品中是否存在共同的模式/簇。通过采用无监督机器学习方法探索样本之间的数据驱动相关性：层次聚类分析 (HCA) 和主成分分析 (PCA)。三个主成分，捕获整个数据集的 81.3% 变化，揭示了系统参数在比热增强百分比中的贡献降序为浓度、温度、密度比和纳米颗粒尺寸。HCA 产生的多元簇显示密度比对温度的相互依赖性，这显着影响纳米流体在较高浓度下的稳定性，导致比热增强百分比下降。此外，发现纳米颗粒尺寸的变化对比热增强的影响可以忽略不计。在较高浓度下稳定，导致比热增强百分比降低。此外，发现纳米颗粒尺寸的变化对比热增强的影响可以忽略不计。在较高浓度下稳定，导致比热增强百分比降低。此外，发现纳米颗粒尺寸的变化对比热增强的影响可以忽略不计。