The optimal synthesis of advanced nanomaterials with numerous reaction parameters, stages, and routes, poses one of the most complex challenges of modern colloidal science, and current strategies often fail to meet the demands of these combinatorially large systems. In response, we present an Artificial Chemist: the integration of machine learning-based experiment selection and high-efficiency autonomous flow chemistry. With the self-driving Artificial Chemist, we autonomously synthesize made-to-measure inorganic perovskite quantum dots (QDs) in flow, and simultaneously tune for their quantum yield and composition polydispersity at target bandgaps, spanning 1.9 eV to 2.9 eV. Utilizing the Artificial Chemist, eleven precision-tailored QD synthesis compositions are obtained without any prior knowledge, within 30 h, using less than 210 mL of total starting QD solutions, and without user selection of experiments. Using the knowledge generated from these studies, we then pre-train the Artificial Chemist to use a new batch of precursors and further accelerate the synthetic path discovery of QD compositions, by at least two-fold. The knowledge transfer strategy further enhances the optoelectronic properties of the in-flow synthesized QDs (within the same resources as the no-prior knowledge experiments) and mitigates the issues of batch-to-batch precursor variability, resulting in QDs averaging within 1 meV from their target bandgap.
Solution-processed organic, [1-3] metallic, [4-6] and semiconductor [7,8] nanomaterials, possess unique size-related physicochemical, optical, magnetic, and electronic properties. These materials have enabled groundbreaking advancements in a variety of applications including catalysis, [9-11] drug delivery, [12,13] data storage, [14] and solar cells. [15] Different nucleation and growth models such as LaMer burst nucleation, [16] Ostwald ripening, [17] Finke-Watzky two-step mechanism, [18] orientated attachment, [19] and coalescence [20] have attempted to explain the mechanisms through which nanoparticles are formed in In recent years, microfluidic technologies have emerged as a powerful approach for the advanced synthesis and rapid optimization of various solution-processed nanomaterials, including semiconductor quantum dots and nanoplatelets, and metal plasmonic and reticular framework nanoparticles. These fluidic systems offer access to previously unattainable measurements and synthesis conditions at unparalleled efficiencies and sampling rates. Despite these advantages, microfluidic systems have yet to be extensively adopted by the colloidal nanomaterial community. To help bridge the gap, this progress report details the basic principles of microfluidic reactor design and performance, as well as the current state of online diagnostics and autonomous robotic experimentation strategies, toward the size, shape, and composition-controlled synthesis of various colloidal nanomaterials. By discussing the application of fluidic platforms in recent high-priority colloidal nanomaterial studies and their potential for integration with rapidly emerging artificial intelligence-based decision-making strategies, this report seeks to encourage interdisciplinary collaborations between microfluidic reactor engineers and colloidal nanomaterial chemists. Full convergence of these two research efforts offers significantly expedited and enhanced nanomaterial discovery, optimization, and manufacturing.
Autonomous robotic experimentation strategies are rapidly rising in use because, without the need for user intervention, they can efficiently and precisely converge onto optimal intrinsic and extrinsic synthesis conditions for...
Closed-loop, autonomous experimentation enables accelerated and material-efficient exploration of large reaction spaces without the need for user intervention. However, autonomous exploration of advanced materials with complex, multi-step processes and data sparse environments remains a challenge. In this work, we present AlphaFlow, a self-driven fluidic lab capable of autonomous discovery of complex multi-step chemistries. AlphaFlow uses reinforcement learning integrated with a modular microdroplet reactor capable of performing reaction steps with variable sequence, phase separation, washing, and continuous in-situ spectral monitoring. To demonstrate the power of reinforcement learning toward high dimensionality multi-step chemistries, we use AlphaFlow to discover and optimize synthetic routes for shell-growth of core-shell semiconductor nanoparticles, inspired by colloidal atomic layer deposition (cALD). Without prior knowledge of conventional cALD parameters, AlphaFlow successfully identified and optimized a novel multi-step reaction route, with up to 40 parameters, that outperformed conventional sequences. Through this work, we demonstrate the capabilities of closed-loop, reinforcement learning-guided systems in exploring and solving challenges in multi-step nanoparticle syntheses, while relying solely on in-house generated data from a miniaturized microfluidic platform. Further application of AlphaFlow in multi-step chemistries beyond cALD can lead to accelerated fundamental knowledge generation as well as synthetic route discoveries and optimization.
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