We recently demonstrated the capabilities of this approach, by creating an AI-guided workflow for finding thermal insulators . For this project we modelled the thermal conductivity, κL, of a material based on its structural, harmonic, and the anharmonic properties (see for a complete list). We then applied feature importance metrics, and found only three inputs were important. From here we were able to map the expected value of κL against each of these inputs to find the screening procedure highlighted in Figure . With this workflow we were able to efficiently find 16 predicted ultra-thermal insulators with a κL less than 1 W/mK out of an initial set of 732 materials .