Skip to main content

Identifying candidate hosts for quantum defects via data mining

Author(s): Ferrenti, AM; de Leon, Nathalie P; Thompson, Jeffrey D; Cava, RJ

To refer to this page use:
Abstract: Atom-like defects in solid-state hosts are promising candidates for the development of quantum information systems, but despite their importance, the host substrate/defect combinations currently under study have almost exclusively been found serendipitously. Here we systematically evaluate the suitability of host materials by applying a combined four-stage data mining and manual screening process to all entries in the Materials Project database, with literature-based experimental confirmation of band gap values. We identify a total of 541 viable hosts (16 unary and 74 binary) for quantum defect introduction and potential use in quantum information systems. This represents a significant (99.57%) reduction from the total number of known inorganic phases, and the application of additional selection criteria for specific applications will reduce their number even further. The screening principles outlined may easily be applied to previously unrealized phases and other technologically important materials systems.
Publication Date: 2020
Citation: Ferrenti, AM, de Leon, NP, Thompson, JD, Cava, RJ. (2020). Identifying candidate hosts for quantum defects via data mining. npj Computational Materials, 6 (10.1038/s41524-020-00391-7
DOI: doi:10.1038/s41524-020-00391-7
Type of Material: Journal Article
Journal/Proceeding Title: npj Computational Materials
Version: Final published version. This is an open access article.

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.