Issue 18, 2022

Accelerating colloidal quantum dot innovation with algorithms and automation

Abstract

Quantum dots (QDs) have received an immense amount of research attention and investment in the four decades since their discovery, and fantastic progress has been made. However, they are complex materials exhibiting distinctive behaviors, and they have been slow to proliferate in real-world applications. QDs occupy an intermediate state of matter, being neither bulk nor molecular materials. Their unique and useful properties arise exactly because of this, but massive challenges in product and device stability and reproducibility also follow as a consequence. Chief amongst the many challenges faced in bringing QD-based devices to market are managing heavy-metal content and device instability. In this review, the possibility of using emerging data-driven methodologies from artificial intelligence (AI) and machine learning (ML) to expedite the translation of QDs from the lab bench to impactful energy-related applications is explored. These approaches will help us go from scarce and patchy knowledge of highly complex parameter spaces to accurate and broad 'maps', intelligently targeted synthesis and advanced quality control.

Graphical abstract: Accelerating colloidal quantum dot innovation with algorithms and automation

Article information

Article type
Review Article
Submitted
26 apr 2022
Accepted
30 iyl 2022
First published
12 avq 2022
This article is Open Access
Creative Commons BY-NC license

Mater. Adv., 2022,3, 6950-6967

Accelerating colloidal quantum dot innovation with algorithms and automation

N. Munyebvu, E. Lane, E. Grisan and P. D. Howes, Mater. Adv., 2022, 3, 6950 DOI: 10.1039/D2MA00468B

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements