Perovskite quantum dots (QDs), have recently emerged as an exciting class of semiconducting materials, outperforming conventional II-VI, IV-VI, and III-V semiconductor nanocrystals in QD-based optoelectronic devices.1 Despite the substantial improvement in performance of perovskite QD-based optoelectronic devices over the past five years, two major obstacles hinder their development: (i) trial and error-based QD synthesis, discovery, and optimization methods, and (ii) poor size dispersity of batch-synthesized colloidal nanocrystals, resulting in surface trap states. Recent advances in machine learning (ML), specifically in supervised/unsupervised learning approaches, e.g., artificial neural networks (NNs) and deep reinforcement learning algorithms, provide an exciting opportunity for reshaping the synthesis and optimization of QDs through the machine-based direction of a high-throughput QD synthesizer. In this work, we designed and developed a ML-enhanced flow synthesis strategy utilizing an automated microfluidic platform2 for autonomous synthesis and optimization of colloidal QDs with optimal optoelectronic properties. The ML-enhanced flow synthesis platform can rapidly and efficiently (i) explore the massive synthesis parameter space of colloidal QDs, (ii) learn the colloidal synthesis and processing scheme of nanocrystals, (iii) identify the composition and relevant synthesis route(s) to achieve specific optoelectronic properties, and (iv) synthesize the optimized QDs at a fraction of time/cost of batch techniques. The developed autonomous flow synthesis technique can be readily adapted for on-demand synthesis and optimization of other solution-processed class of nanoparticles, thereby accelerating the nanomaterial discovery and optimization process.

Machine learning-enhanced flow synthesis of quantum dots

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.