A multi-objective platform for autonomous targeting and optimization of colloidal quantum dots

The optimization of colloidal quantum dot (CQD) materials, synthesis routes and processing methods are complex challenges that are ripe for automation and artificial intelligence (AI) to have a great impact. These optimization challenges are seldom oriented to a single target, therefore it is vital that autonomous systems can handle multiple objectives. In this work, we present an autonomous CQD synthesis system that successfully performs multi-objective optimization (MOO) via Bayesian optimization-based algorithms. We demonstrate the efficacy of the system through three distinct synthesis challenges, based on one, two and three objective optimization problems, in the synthesis of cesium lead halide perovskite CQDs. Objectives included maximizing fluorescence brightness, minimizing particle size dispersity, and targeting of specific energy bandgaps and particle diameters. The tri-objective challenge achieved simultaneous targeting of specific CQD sizes and band gaps independently via reaction tuning and halide doping, while minimizing the particle size dispersity. To the best of our knowledge, this is the first demonstration of AI-assisted multi-objective targeting and dynamic synthesis of targeted colloidal CQDs using exciton energy analysis of absorption spectra infer both size and bandgap. This work presents an accessible, automated, and data-driven platform for CQD discovery and optimization—both for single and multiple objectives—highlighting the promise of widespread integration of AI-guided strategies into CQD R&D.