Autonomous nanomanufacturing of lead-free metal halide perovskite nanocrystals using a self-driving fluidic lab

Synthesis of Cs3Cu2I5 Nanocrystals in a Continuous Flow System

Achieving the goal of generating all of the world's energy via renewable sources and significantly reducing the energy usage will require the development of novel, abundant, nontoxic energy conversion materials. Here, a cost-efficient and scalable continuous flow synthesis of Cs3Cu2I5 nanocrystals is developed as a basis for the rapid advancement of novel nanomaterials. Ideal precursor solutions are obtained through a novel batch synthesis, whose product served as a benchmark for the subsequent flow synthesis. Realizing this setup enabled a reproducible fabrication of Cs3Cu2I5 nanocrystals. The effect of volumetric flow rate and temperature on the final product's morphology and optical properties are determined, obtaining 21% quantum yield with the optimal configuration. Consequently, the size and morphology of the nanocrystals can be tuned with far more precision and in a much broader range than previously achievable. The flow setup is readily applicable to other relevant nanomaterials. It should enable a rapid determination of a material's potential and subsequently optimize its desired properties for renewable energy generation or efficient optoelectronics.

Active Learning of Ligands That Enhance Perovskite Nanocrystal Luminescence

Ligands play a critical role in the optical properties and chemical stability of colloidal nanocrystals (NCs), but identifying ligands that can enhance NC properties is daunting, given the high dimensionality of chemical space. Here, we use machine learning (ML) and robotic screening to accelerate the discovery of ligands that enhance the photoluminescence quantum yield (PLQY) of CsPbBr3 perovskite NCs. We developed a ML model designed to predict the relative PL enhancement of perovskite NCs when coordinated with a ligand selected from a pool of 29,904 candidate molecules. Ligand candidates were selected using an active learning (AL) approach that accounted for uncertainty quantified by twin regressors. After eight experimental iterations of batch AL (corresponding to 21 initial and 72 model-recommended ligands), the uncertainty of the model decreased, demonstrating an increased confidence in the model predictions. Feature importance and counterfactual analyses of model predictions illustrate the potential use of ligand field strength in designing PL-enhancing ligands. Our versatile AL framework can be readily adapted to screen the effect of ligands on a wide range of colloidal nanomaterials.

Performance metrics to unleash the power of self-driving labs in chemistry and materials science

With the rise of self-driving labs (SDLs) and automated experimentation across chemical and materials sciences, there is a considerable challenge in designing the best autonomous lab for a given problem based on published studies alone. Determining what digital and physical features are germane to a specific study is a critical aspect of SDL design that needs to be approached quantitatively. Even when controlling for features such as dimensionality, every experimental space has unique requirements and challenges that influence the design of the optimal physical platform and algorithm. Metrics such as optimization rate are therefore not necessarily indicative of the capabilities of an SDL across different studies. In this perspective, we highlight some of the critical metrics for quantifying performance in SDLs to better guide researchers in implementing the most suitable strategies. We then provide a brief review of the existing literature under the lens of quantified performance as well as heuristic recommendations for platform and experimental space pairings.