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 4 2022
Accepted
30 7 2022
First published
12 8 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

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