Synthesizer: Chemistry‐Aware Machine Learning for Precision Control of Nanocrystal Growth
Abstract
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.
Article Details
Authors (12)
Nina A. Henke
Nanospectroscopy Group and Center for NanoScience (CeNS) Nanoinstitute Munich Department of Physics Ludwig‐Maximilians‐Universität (LMU) München 80539 Munich Germany
Leo Luber
Nanospectroscopy Group and Center for NanoScience (CeNS) Nanoinstitute Munich Department of Physics Ludwig‐Maximilians‐Universität (LMU) München 80539 Munich Germany
Ioannis Kouroudis
Chair of Simulation of Nanosystems for Energy Conversion, Department of Electrical Engineering TUM School of Computation, Information, Technology Atomistic Modeling Center (AMC) Munich Data Science Institute (MDSI) Technische Universität München (TUM) 85748 Garching Germany
Jonathan Paul
Nanospectroscopy Group and Center for NanoScience (CeNS) Nanoinstitute Munich Department of Physics Ludwig‐Maximilians‐Universität (LMU) München 80539 Munich Germany
Alexander Schuhbeck
Nanospectroscopy Group and Center for NanoScience (CeNS) Nanoinstitute Munich Department of Physics Ludwig‐Maximilians‐Universität (LMU) München 80539 Munich Germany
Lukas M. Rescher
Soft Condensed Matter Group and Center for NanoScience (CeNS) Department of Physics Ludwig‐Maximilians‐Universität (LMU) München 80539 Munich Germany
Tizian Lorenzen
Department of Chemistry and Center for NanoScience, Ludwig-Maximilians-Universität München 1 , Butenandtstr. 11, 81377 München,
Veronika Mayer
Nanospectroscopy Group and Center for NanoScience (CeNS) Nanoinstitute Munich Department of Physics Ludwig‐Maximilians‐Universität (LMU) München 80539 Munich Germany
Knut Müller‐Caspary
Department of Chemistry and Center for NanoScience (CeNS) Ludwig‐Maximilians‐Universität (LMU) München 81377 Munich Germany
Bert Nickel
Alessio Gagliardi
Chair of Simulation of Nanosystems for Energy Conversion, Department of Electrical Engineering TUM School of Computation, Information, Technology Atomistic Modeling Center (AMC) Munich Data Science Institute (MDSI) Technische Universität München (TUM) 85748 Garching Germany
Alexander S. Urban
Nanospectroscopy Group and Center for NanoScience (CeNS) Nanoinstitute Munich Department of Physics Ludwig‐Maximilians‐Universität (LMU) München 80539 Munich Germany