Synthesizer: Chemistry‐Aware Machine Learning for Precision Control of Nanocrystal Growth

Abstract Precise and reproducible control over nanocrystal synthesis is essential for tailoring optical properties, yet remains a long‐standing challenge in halide perovskites. A broadly adoptable machine learning–guided framework, the Synthesizer, is introduced that combines Gaussian Process regression and Bayesian optimization with chemistry‐aware molecular encodings and systematic feature engineering. Rather than new algorithms, the advance lies in translating interpretable machine learning tools into a practical, benchtop platform for nanocrystal optimization under ambient conditions. Using CsPbBr 3 as a model system, nm‐level precision in photoluminescence peak tuning (430 nm to 520 nm) is achieved, along with benchmark narrow linewidths down to 70 meV via lateral confinement control, and robust photoluminescence quantum yield optimization linked to surface trap density. Mapping the two‐dimensional parameter space (Cs/PbBr 2 and antisolvent/PbBr 2 ratios) across multiple antisolvents enables predictive optimization and identifies the antisolvent/PbBr 2 ratio as a previously underappreciated mechanistic parameter, offering a quantitative basis for antisolvent‐accelerated nanocrystal growth. Transfer tests across distinct chemical spaces, including alcohols and cyclopentanone, confirm generalizability to unseen molecules, while application to CsPbI 3 demonstrates extension to new material systems. These results establish an adoption‐ready platform for data‐efficient, uncertainty‐aware synthesis design, providing reproducible pathways to accelerate materials discovery beyond halide perovskites.