Dataset for surrogate-assisted multi-objective optimization of fin geometries in hydrate-based thermochemical energy storage

This dataset contains 320 simulation cases generated using COMSOL Multiphysics for training and validation of a machine-learning surrogate model. Each case includes geometric parameters of branched-fin structures and the corresponding thermal performance metrics, including total heat storage capacity and thermal power. The dataset is intended to support transparency, reproducibility, and comparative studies of surrogate-assisted optimization in hydrate-based thermochemical energy storage systems.