Predicting solid state material platforms for quantum technologies
OPEN ACCESS
Loading...
Author / Producer
Date
2022-09-28
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
Semiconductor materials provide a compelling platform for quantum technologies (QT). However, identifying promising material hosts among the plethora of candidates is a major challenge. Therefore, we have developed a framework for the automated discovery of semiconductor platforms for QT using material informatics and machine learning methods. Different approaches were implemented to label data for training the supervised machine learning (ML) algorithms logistic regression, decision trees, random forests and gradient boosting. We find that an empirical approach relying exclusively on findings from the literature yields a clear separation between predicted suitable and unsuitable candidates. In contrast to expectations from the literature focusing on band gap and ionic character as important properties for QT compatibility, the ML methods highlight features related to symmetry and crystal structure, including bond length, orientation and radial distribution, as influential when predicting a material as suitable for QT.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
8 (1)
Pages / Article No.
207
Publisher
Nature
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Computational methods; Electronic properties and materials; Optical materials and structures; Semiconductors