Accelerating colloidal quantum dot innovation with algorithms and automation

Philip Howes, Ngonidzashe Neal, Esme Willow, Enrico Grisan

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
3 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)6950-6967
Number of pages18
JournalMaterials Advances
Volume3
Issue number18
DOIs
Publication statusPublished - 12 Aug 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s).

Keywords

  • microfluidics
  • machine learning
  • automation
  • nanoparticles

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