(696g) Autonomous Synthesis of Metal Halide Perovskite Nanocrystals | AIChE

(696g) Autonomous Synthesis of Metal Halide Perovskite Nanocrystals


Bateni, F. - Presenter, Ohio University
Epps, R., North Carolina State University
Abdel-Latif, K., North Carolina State University
Bennett, J., North Carolina State University
Reyes, K. G., University at Buffalo
Abolhasani, M., NC State University
Metal halide perovskite (MHP) nanocrystals (NCs), owing to their facile solution-processibility and tunable optoelectronic properties, are considered an emerging class of active materials for next-generation clean energy technologies. [1] Metal cation doping of MHP NCs with transition-metal ions (e.g., manganese, Mn2+) is an attractive route to not only substitute heavy metal ions (Pb2+) with less toxic impurity dopants, but also introduce new optoelectronic and magnetic properties into the host NCs. [2]

Discovery and development of metal cation-doped MHP NCs is conventionally achieved using batch reactors (flasks) with offline characterization. [1] However, flask-based NC synthesis is time-, material- and labor-intensive, and suffers from batch-to-batch variations, non-uniform heat/mass transfer rates, and difficulty of integration with in situ NC characterization probes. [1] In addition, the complex and vast design space of metal cation-doped MHP NCs further complicate their fundamental mechanistic studies and hampers the search for finding the optimal synthetic route using the trial-and-error one at a time experimentation approach. [3]

The emergence of artificial intelligence (AI)-assisted experimental space exploration strategies have provided an exciting opportunity to accelerate navigation through the high-dimensional experimental space of emerging advanced functional materials through convergence of AI with colloidal nanoscience and automated experimentation. The result of such convergence is ‘self-driving laboratories’ for closed-loop autonomous exploration and/or exploitation of the experimental space. [3] In particular, deep neural networks (DNNs) have proven to be an effective machine learning (ML) technique for accelerated fundamental and applied studies of materials with high-dimensional design space, including semiconductor NCs.[3] Microreactors with their reduced chemical consumption and waste generation, reproducible and enhanced heat/mass transfer rates, and facile integration with in situ NC characterization techniques are an ideal reactor type for controlled synthesis of MHP NCs. [1]

In this work, we present a self-driving laboratory using modular flow reactors for accelerated design space exploration, synthetic route discovery, and fundamental mechanistic studies of metal cation-doped MHP NCs. Specifically, we developed a two-stage sequential halide exchange and cation doping of MHP NCs without the need for an intermediate washing stage. Next, we built an AI model of the two-stage flow synthesis platform (i.e., digital twin of the experimental platform) using 60 autonomously conducted NC synthesis experiments. We then utilized the developed digital twin of the self-driving lab to: (i) identify the key experimental input parameters controlling the metal cation doping of MHP NCs, (ii) unveil the mechanism of the metal cation doping of MHP NCs , and (iii) synthesize Mn-doped MHP NCs on-demand with a targeted doping level. The developed self-driving lab can rapidly identify the optimal synthetic route for in-flow synthesis of MHP NCs with a desired Mn doping level in less than 90 min. The self-driving lab detailed in this work, presents a generic modular framework for autonomous formulation discovery, synthesis, optimization, and continuous manufacturing of novel advanced functional materials for applications in clean energy technologies.


[1] Abdel-Latif, K.; Bateni, F.; Crouse, S.; Abolhasani, M. Flow Synthesis of Metal Halide Perovskite Quantum Dots: From Rapid Parameter Space Mapping to AI-Guided Modular Manufacturing. Matter 2020, 3 (4), 1053–1086. https://doi.org/10.1016/j.matt.2020.07.024.

[2] Bateni, F.; Epps, R. W.; Abdel-latif, K.; Dargis, R.; Han, S.; Volk, A. A.; Ramezani, M.; Cai, T.; Chen, O.; Abolhasani, M. Ultrafast Cation Doping of Perovskite Quantum Dots in Flow. Matter 2021, S2590238521002174. https://doi.org/10.1016/j.matt.2021.04.025.

[3] Bateni, F.; Epps, R. W.; Antami, K.; Dargis, R.; Bennett, J. A.; Reyes, K. G.; Abolhasani, M. Autonomous Nanocrystal Doping by Self‐Driving Fluidic Micro‐Processors. Advanced Intelligent Systems 2022, 2200017. https://doi.org/10.1002/aisy.202200017.