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Evolutionary search for new high‐ k dielectric materials: methodology and applications to hafnia‐based oxides
Author(s) -
Zeng Qingfeng,
Oganov Artem R.,
Lyakhov Andriy O.,
Xie Congwei,
Zhang Xiaodong,
Zhang Jin,
Zhu Qiang,
Wei Bingqing,
Grigorenko Ilya,
Zhang Litong,
Cheng Laifei
Publication year - 2014
Publication title -
acta crystallographica section c
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 17
ISSN - 2053-2296
DOI - 10.1107/s2053229613027861
Subject(s) - dielectric , microelectronics , capacitor , fitness function , high κ dielectric , evolutionary algorithm , computer science , materials science , genetic algorithm , optoelectronics , physics , artificial intelligence , machine learning , voltage , quantum mechanics
High‐ k dielectric materials are important as gate oxides in microelectronics and as potential dielectrics for capacitors. In order to enable computational discovery of novel high‐ k dielectric materials, we propose a fitness model (energy storage density) that includes the dielectric constant, bandgap, and intrinsic breakdown field. This model, used as a fitness function in conjunction with first‐principles calculations and the global optimization evolutionary algorithm USPEX, efficiently leads to practically important results. We found a number of high‐fitness structures of SiO 2 and HfO 2 , some of which correspond to known phases and some of which are new. The results allow us to propose characteristics (genes) common to high‐fitness structures – these are the coordination polyhedra and their degree of distortion. Our variable‐composition searches in the HfO 2 –SiO 2 system uncovered several high‐fitness states. This hybrid algorithm opens up a new avenue for discovering novel high‐ k dielectrics with both fixed and variable compositions, and will speed up the process of materials discovery.