Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores

Alexandra M Goryaeva (1) , Clovis Lapointe (1) , Chendi Dai (1) , Julien Deres (1) , Jean-Bernanrd Maillet (2) , Mihai Cosmin Marinica (1)

1 SRMP - Service de recherches de métallurgie physique
2 DAM/DIF - DAM Île-de-France

This work revises the concept of defects in crystalline solids and proposes a universal strategy for their characterization at the atomic scale using outlier detection based on statistical distances. The proposed strategy provides a generic measure that describes the distortion score of local atomic environments. This score facilitates automatic defect localization and enables a stratified description of defects, which allows to distinguish the zones with different levels of distortion within the structure. This work proposes applications for advanced materials modelling ranging from the surrogate concept for the energy per atom to the relevant information selection for evaluation of energy barriers from the mean force. Moreover, this concept can serve for design of robust interatomic machine learning potentials and high-throughput analysis of their databases. The proposed definition of defects opens up many perspectives for materials design and characterisation, promoting thereby the development of novel techniques in materials science.

Domaines: Matériaux; Matière Condensée [cond-mat]; Machine Learning [stat.ML]

Alexandra M Goryaeva, Clovis Lapointe, Chendi Dai, Julien Deres, Jean-Bernanrd Maillet, et al.. Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores. Nature Communications, 2020, 11 (1), pp.4691. ⟨10.1038/s41467-020-18282-2⟩. ⟨cea-03775679⟩