  The thermal modulator based on the near-field radiative heat transfer has
wide applications in thermoelectric diodes, thermoelectric transistors, and
thermal storage. However, the design of optimal near-field thermal radiation
structure is a complex and challenging problem due to the tremendous number of
degrees of freedom. In this work, we have proposed a data-driven machine
learning workflow to efficiently design multilayer hyperbolic metamaterials
composed of ${\alpha}$-MoO$_{\rm 3}$ for near-field thermal radiative modulator
with high modulation contrast. By combining the multilayer perceptron and
Bayesian optimization, the rotation angle, layer thickness and gap distance of
the multilayer metamaterials are optimized to achieve a maximum thermal
modulation contrast ratio of 6.29. This represents a 97% improvement compared
to previous single layer structure. The large thermal modulation contrast is
mainly attributed to the alignment and misalignment of hyperbolic plasmon
polaritons and hyperbolic surface phonon polaritons of each layer controlled by
the rotation. The results provide a promising way for accelerating the
designing and manipulating of near-field radiative heat transfer by anisotropic
hyperbolic materials through the data-driven style.
