Speaker
Description
Artificial intelligence (AI) frameworks that are capable of creating reliable and interpretable models are paramount for discovering new functional materials. Here, we present the sure independence screening and sparsifying operator (SISSO) [1] and the subgroup discovery (SGD) [2] approaches. Both methods identify analytical equations (SISSO) or Boolean (SGD) expressions from a set of user-given physical parameters, which are able to model a chosen property. In particular we will highlight a new implementation of SISSO, SISSO++ [3], which provides not only a high-performance library for SISSO, but also a user-friendly python interface that facilitate interfacing SISSO into existing frameworks. Finally, we showcase the ability of both of these approaches by applying them to better understand thermal conductivity and catalysis.
References
[1] R. Ouyang, S. Curtarolo, E. Ahmetcik, M. Scheffler, and L. M. Ghiringhelli, SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates, Phys. Rev. Mat. 2, 083802 (2018). https://doi.org/10.1103/PhysRevMaterials.2.083802
[2] B. R. Goldsmith, M. Boley, J. Vreeken, M. Scheffler, and L. M. Ghiringhelli, Uncovering structure-property relationships of materials by subgroup discovery, New. J. Phys. 19, 013031 (2017). https://doi.org/10.1088/1367-2630/aa57c2
[3] T. A. R. Purcell, M. Scheffler, C. Carbogno, and L. M. Ghiringhelli, SISSO++: A C++ Implementation of the
Sure-Independence Screening and Sparsifying Operator Approach, J. Open Source. Softw. 7, 3960 (2022). https://doi.org/10.21105/joss.03960
Address
(a) Present address: The FAIRmat Consortium of the NFDI, Humboldt-Universität zu Berlin, Berlin, Germany
| Abstract Number (department-wise) | SG 05 |
|---|---|
| Department | Scheffler Group |
