Machine Learning‐Assisted Microfluidic Synthesis of Perovskite Quantum Dots

Abstract

The quality and property control of nanomaterials are center themes to guarantee and promote their applications. Different synthesis methods and reaction parameters are control factors for their properties. However, the vast combination number of the factors with multilevels leads to the obstacle that trying all‐through the data space is nearly impossible. Herein, the combination of microfluidic synthesis method with machine learning (ML) models to address this challenge in case of perovskite quantum dots (PQDs) with tunable photoluminescence (PL) is reported. The ML‐assisted synthesis not only helps to elucidate the nucleation growth‐ripening mechanisms, but also successfully guides to synthesize PQDs with precise wavelength and full width of half maximum (FWHM) of the PL by optimizable conditions to match the time‐saving, energy‐saving, and minimal environmental pressure goals.

Keywords

• machine learning
• microfluidic syntheses
• perovskites
• photoluminescence
• quantum dots