Journal
CRYSTALS
Volume 11, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/cryst11091039
Keywords
crystal structures; material discovery; deep learning; feature engineering; multitask learning
Funding
- Artificial Intelligence Lab of the Institute of Physics, UNAM
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The study demonstrated the excellent performance of artificial neural networks developed based on crystal-site features in classifying compounds, retrieving missing compounds, and being suitable for multitask learning paradigms.
In materials science, crystal structures are the cornerstone in the structure-property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the different atomic environments in a crystal compound can be constructed as input data for artificial neural networks (ANNs). In this article, we show the performance of a series of ANNs developed using crystal-site-based features. These ANNs were developed to classify compounds into halite, garnet, fluorite, hexagonal perovskite, ilmenite, layered perovskite, -o-tp- perovskite, perovskite, and spinel structures. Using crystal-site-based features, the ANNs were able to classify the crystal compounds with a 93.72% average precision. Furthermore, the ANNs were able to retrieve missing compounds with one of these archetypical structure types from a database. Finally, we showed that the developed ANNs were also suitable for a multitask learning paradigm, since the extracted information in the hidden layers linearly correlated with lattice parameters of the crystal structures.
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