We present details of the two datasets for the ITR model training and prediction, one is the "ITR dataset" and the other one is the "descriptor dataset" of various materials. The former dataset shows the ITR values of various interfaces with the measuring temperature, synthesized method, thermal measurement method, sample pre-treatment and its original references. The latter dataset shows the physical, chemical and process descriptors of 298 different materials, which are single element or binary compounds. These materials can construct over 80,000 pair-material systems (e.g. Bi/Si) for ITR prediction. Additionally, the total energy for the binding energy can also be found in the "atom_energy_vasp" file. Further, the training data for the ITR machine-learning model are furnished under the file name“training dataset for ITR prediction” and can be directly used as training data for ITR predictions. Descriptor dataset for predicting interfacial thermal resistance is licensed under a Creative Commons Attribution 4.0 International License.

{"references": ["Yen-Ju Wu, Lei Fang, Yibin Xu, npj Computational Materialsvolume 5, Article number: 56 (2019)", "Wu, Y., Zhan, T., Hou, Z. et al. Physical and chemical descriptors for predicting interfacial thermal resistance. Sci Data 7, 36 (2020)."]}