This talk describes recent developments of glass-box artificial-intelligence (AI) methods for learning the key descriptive parameters that are correlated with the processes that trigger, facilitate, or hinder the material’s performance.[1, 2, 3]
As an example, we demonstrate the power of the SISSO approach for the description of the lattice thermal conductivity and hierarchically screen over hundreds of materials, while minimizing the computational cost. Several ultra-insulating materials are identified, 15 of them with a predicted thermal conductivity even smaller than 1 W/mK.
1) C. Draxl and M. Scheffler, Big-Data-Driven Materials Science and its FAIR Data Infrastructure, in Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer (2020). https://doi.org/10.1007/978-3-319-44677-6_104
2) B. R. Goldsmith et al. New J. Phys. 19, 013031 (2017). https://doi.org/10.1088/1367-2630/aa57c2
3) R. Ouyang et al. J. Phys. Mater. 2, 024002 (2019). https://doi.org/10.1088/2515-7639/ab077b
4) T. Purcell, et al. https://arxiv.org/abs/2204.12968