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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Hume-Rothery Symposium on First-Principles Materials Design
Presentation Title Learning Rules for High-Throughput Screening of Materials Properties and Functions
Author(s) Thomas A. R. Purcell , Matthias Scheffler
On-Site Speaker (Planned) Matthias Scheffler
Abstract Scope 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.[4] 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). 2) B. R. Goldsmith et al. New J. Phys. 19, 013031 (2017). 3) R. Ouyang et al. J. Phys. Mater. 2, 024002 (2019). 4) T. Purcell, et al.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Computational Materials Science & Engineering,


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