Evolutionary search for new high-k dielectric materials: methodology and applications to hafnia-based oxides

TL;DR

This paper introduces a computational method combining a fitness model, first-principles calculations, and evolutionary algorithms to discover new high-k dielectric materials, successfully identifying known and novel structures.

Contribution

It develops a hybrid computational approach for discovering high-k dielectrics, integrating a fitness model with evolutionary algorithms and first-principles calculations.

Findings

Identified high-fitness structures of SiO2 and HfO2, including known and new phases.

Proposed structural features common to high-fitness materials, such as specific coordination polyhedra.

Uncovered promising high-fitness states in the HfO2-SiO2 system.

Abstract

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 fitness function in conjunction with first-principles calculations and global optimization evolutionary algorithm USPEX, efficiently leads to practically important results. We found a number of high-fitness structures of SiO2 and HfO2, some of which correspond to known phases and some 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 HfO2-SiO2 system uncovered several high-fitness states. This hybrid algorithm opens up a new avenue of discovering novel high-k dielectrics with both fixed and variable compositions, and will speed up the process of materials discovery.