Regensburg 2019 – wissenschaftliches Programm

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MM: Fachverband Metall- und Materialphysik

MM 33: Topical session (Symposium MM): Big Data Analytics in Materials Science

MM 33.8: Vortrag

Donnerstag, 4. April 2019, 12:45–13:00, H43

Artificial intelligence in materials science: towards optimal descriptors — •Benedikt Hoock1,2, Santiago Rigamonti1, Luca Ghiringhelli2, Matthias Scheffler1,2, and Claudia Draxl1,21Humboldt-Universität zu Berlin, Berlin, DE — 2Fritz-Haber-Institut der MPG, Berlin, DE

Materials data contained in repositories like NOMAD [1] can be exploited in many useful ways, such as to better understand existing materials or to discover new materials with desired properties. A crucial step towards these goals is to find a set of meaningful descriptors, i.e. parameters based on computationally cheap input data that capture the physical mechanisms underlying certain material properties. In this work, we develop principles for constructing up to millions of candidate descriptors from simple physical properties. These principles involve mathematical operations [2] and different averaging procedures considering the local ordering. We compare two compressed sensing methods, LASSO+ℓ0 [2] and SISSO [3], at identifying optimal descriptors out of all the candidates. Likewise, we introduce and compare cross-validation based model-selection strategies that use either the average training or the average test error as a criterion, aiming at increasing the descriptors’ generalizability. We use two ab initio data sets, comprising group-IV zincblende ternaries and transparent conducting oxides, to test this methodological approaches.

[1]: C. Draxl & M. Scheffler, MRS Bulletin, 43, 676 (2018).

[2]: L. M. Ghiringhelli, et. al., Phys. Rev. Lett. 114, 105503 (2015).

[3]: R. Ouyang, et. al., Phys. Rev. Mater. 2, 083802 (2018).

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