Data for "A multi-objective platform for autonomous property targeting and optimization of colloidal lead halide perovskite quantum dots"

Raw data associated with the publication Multiobjective Platform for Autonomous Property Targeting and Optimization of Colloidal Lead Halide Perovskite Quantum Dots (Chemistry of Materials, Aug 2025). The raw data is labelled with reference to the figures in the papers, and is provided in its most accessible form. The majority of the data is spectral data from either absorption or photoluminescence spectroscopy, with photon energy (eV) on the x axis. TEM images are included as raw files from the respective instruments, in .tif format. Python files are included where appropriate.AbstractThe 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 optical bandgap and particle diameter. 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.The work demonstrates the use of AI-assisted multi-objective targeting and dynamic synthesis of targeted colloidal CQDs using exciton energy analysis of absorption spectra to infer both size and optical 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.