Accelerating colloidal quantum dot innovation with algorithms and automation-Reference-Cited by-同舟云学术

Accelerating colloidal quantum dot innovation with algorithms and automation

Published:2022 Issue:18 Volume:3 Page:6950-6967
ISSN:2633-5409
Container-title:Materials Advances
language:en
Short-container-title:Mater. Adv.

Author:

Munyebvu Neal¹^ORCID,Lane Esmé¹^ORCID,Grisan Enrico²^ORCID,Howes Philip D.¹^ORCID

Affiliation:

1. Division of Mechanical Engineering and Design, School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK

2. Division of Computer Science and Informatics, School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK

Abstract

This review discusses how high-throughput experimentation and data-driven strategies, such as the use of machine learning models, are being used to enable rapid advances in colloidal quantum dot technologies.

Funder

Royal Society of Chemistry

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Chemistry (miscellaneous)

Link

http://pubs.rsc.org/en/content/articlepdf/2022/MA/D2MA00468B

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