Accelerating colloidal quantum dot innovation with algorithms and automation. Issue 18 (12th August 2022) - Munyebvu, Neal; Lane, Esmé; Grisan, Enrico; Howes, Philip D.
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. 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.