Deep learning techniques elucidate and modify the shape factor to extend the effective medium theory beyond its original formulation

•Deep learning elucidates the shape factor in EMT for thermal conductivity estimates.•The ratio of an inclusion’s projected areas is closely related to the shape factor.•Transfer learning extends the original EMT for new thermal transport problems. The effective medium theories (EMTs) can reliably approximate the property of a composite using properties of the inclusion and matrix phase. However, their inherent assumptions and the availability of mathematical forms for describing the inclusion structure limit their accuracy and applicability. In this work, we utilize the capabilities of a deep learning method to ameliorate the latter restriction for a particular EMT formulation. Our deep learning models elucidate the inclusion structure using several physics-based descriptors and can be easily adapted for other inclusion shapes through transfer learning. Using our models, we shed light on the interpretation of the shape factor in the chosen EMT. More importantly, we extend, not replace, the EMT for cases beyond its original formulation. Our proposed transfer learning approach requires relatively low computation cost and a small sample number, making it especially useful when new data is limited.