The trend in miniaturization and enhanced functional performance of integrated circuits and power electronics and photonics has amplified the generated thermal energy in these devices making thermal management a bottleneck for further advancement in these fields. A range of geometries of hierarchical structures are developed and examined to address this challenge. However, the numerous form factors and dimension of hierarchical structures in addition to cost and time-consuming synthesis and test procedures make it unfeasible to explore bountiful variations of hierarchical geometries through experimental methods. Here, we introduce a general Artificial Intelligence (AI) platform to address this challenge and guide discovery of hierarchical structures for extreme thermal management of high-performance photonics/electronics. The AI platform is based on Random Forest (RF) algorithm, a robust AI method, and was trained using a large collected experimental data set corresponding to thin film evaporation in various forms of hierarchical structures. Four geometrical dimensions of the hierarchical structures and two dimensionless numbers governing heat transfer and fluid dynamics in these structures were used as independent variables to predict heat flux in these structures. The trained model's performance was analyzed using statistical metrics and showed an excellent prediction of heat flux for all the structures with various working fluids. The performance of predictive AI platform was further validated by two independent studies of different research groups. This predictive platform provides a foundation for rational discovery of hierarchical structures and working fluids to address the ongoing challenge of thermal management in broad spectrum of technologies including electronics, hypersonic aviation and electric vehicles.

集成电路以及电力电子和光子学的小型化和功能性能增强的趋势已经放大了这些器件中产生的热能，使热管理成为这些领域进一步发展的瓶颈。开发并检查了一系列层次结构的几何形状以应对这一挑战。然而，除了成本和费时的合成和测试程序外，层级结构的众多形状因素和尺寸还使得通过实验方法探索层级几何结构的丰富变化变得不可行。在这里，我们介绍了一个通用的人工智能（AI）平台来应对这一挑战，并指导发现用于高性能光子学/电子学的极端热管理的分层结构。AI平台基于鲁棒的AI方法随机森林（RF）算法，并使用与各种形式的分层结构中的薄膜蒸发相对应的大量实验数据集进行了训练。分层结构的四个几何尺寸和控制这些结构中的传热和流体动力学的两个无量纲数被用作独立变量，以预测这些结构中的热通量。使用统计指标对训练后的模型的性能进行了分析，并显示了具有各种工作流体的所有结构的热通量的出色预测。预测性AI平台的性能通过不同研究组的两项独立研究得到了进一步验证。