Written language and conventional data structures for representing scientific procedures suffer from low process detail, often fail to accurately represent protocols, and lack universality. New strategies for the handling of experimental data are needed to provide viable process information for both humans and machines. In this work, we present the universal workflow language (UWL) and interface (UWLi). UWL is afindable, accessible, interoperable, and reusable (FAIR)-compatible, graph-based data architecture that can capture arbitrary scientific procedures through workflow representation, and UWLi is an accompanying software package for building, manipulating, and interpreting UWL entries. The UWL format was found to be highly effective in identifying deficiencies in the reported process details of high-impact, peer-reviewed scientific journals, and in simulated scenarios, the graph format was shown to be more effective than conventional methods in predictively modeling the outcome of diverse scientific protocols. Implementation of UWL could enable more accurate scientific communication and more impactful process datasets.

Autonomous science is transforming how we discover materials and chemical systems for advanced energy technologies. However, many initially promising systems never reach deployment. This "valley of death" stems from optimization that prioritizes laboratory metrics over industrial viability. We propose a new strategy: "born-qualified" autonomous development, which embeds manufacturability, cost, and durability constraints from the outset. This approach is enabled by four pillars, including the development of multi-objective metrics, causal models, a modular infrastructure, and embedding manufacturing in the discovery loop. Realizing this vision will require sustained, community-wide commitment, but the potential return on that investment is commensurate with the scale of the challenge.

The engineering of mixed-solvent formulations and their evaporation conditions are key to reproducible perovskite coatings for high-performance photovoltaics. Here, we report a lumped-parameter evaporation model to predict the evolution of a perovskite ink liquid film over time (solvent ratio, solute concentration, and film thickness). The drying-rate model is validated via in situ film-thickness measurements, and the dicted transient liquid film state is mapped as a process path. These methods allow for the prediction of cess sensitivity to local environmental factors and the understanding and visualization of a broader processing parameter space enabled through the coupling of process and ink engineering. Process maps are applied to create a new framework for scalable perovskite coating development with a goal of improving the reproducibility and transferability of perovskite fabrication. This approach is demonstrated with blade-coated FA0.83Cs0.17PbI3 photovoltaic devices, improving the photovoltaic conversion efficiency from 17.5% 1.7% to 20.3% +/- 0.6%.

This work presents the use of graph learning for the prediction of multi-step experimental outcomes for applications across experimental research, including material science, chemistry, and biology. The viability of geometric learning for regression tasks was benchmarked against a collection of linear models through a combination of simulated and real-world data training studies. First, a selection of five arbitrarily designed multi-step surrogate functions were developed to reflect various features commonly found within experimental processes. A graph transformer network outperformed all tested linear models in scenarios that featured hidden interactions between process steps and sequence dependent features, while retaining equivalent performance in sequence agnostic scenarios. Then, a similar comparison was applied to real-world literature data on algorithm guided colloidal atomic layer deposition. Using the complete reaction sequence as training data, the graph neural network outperformed all linear models in predicting the three spectral properties for most training set sizes. Further implementation of graph neural networks and geometric representation of scientific processes for the prediction of experiment outcomes could lead to algorithm driven navigation of higher dimension parameter spaces and efficient exploration of more dynamic systems.

The vast chemical space of emerging semiconductors, like metal halide perovskites, and their varied requirements for semiconductor applications have rendered trial-and-error environmentally unsustainable. In this work, we demonstrate RoboMapper, a materials acceleration platform (MAP), that achieves 10-fold research acceleration by formulating and palletizing semiconductors on a chip, thereby allowing high-throughput (HT) measurements to generate quantitative structure-property relationships (QSPRs) considerably more efficiently and sustainably. We leverage the RoboMapper to construct QSPR maps for the mixed ion FA1 _yCsyPb(I1 ,Brx)3 halide perovskite in terms of structure, bandgap, and photostability with respect to its composition. We identify wide-bandgap alloys suitable for perovskite-Si hybrid tandem solar cells exhibiting a pure cubic perovskite phase with favorable defect chemistry while achieving superior stability at the target bandgap of -1.7 eV. RoboMapper's palletization strategy reduces environmental impacts of data generation in materials research by more than an order of magnitude, paving the way for sustainable data-driven materials research.

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.

Development and scalable nanomanufacturing of high‐quality heavy metal‐free quantum dots (QDs) with high‐dimensional experimental design spaces still remain a challenge. In this work, a universal flow chemistry framework for accelerated fundamental and applied studies of heavy metal‐free QDs with multi‐stage chemistries is presented. By introducing flexible time‐ and temperature‐to‐distance transformation using modular fluidic blocks, an in‐flow synthetic route of InP QDs with the highest reported first excitonic absorption peak to valley ratio is unveiled with a reaction time one order of magnitude faster than batch reactors. The flexible time‐ and temperature‐to‐distance transformation as an enabling factor for generalization of flow reactors toward the accelerated discovery, development, and nanomanufacturing of high‐quality emerging nanomaterials for next‐generation energy, display, and chemical technologies is discussed.

Lead halide perovskite (LHP) nanocrystals (NCs) are considered an emerging class of advanced functional materials with numerous outstanding optoelectronic characteristics. Despite their success in the field, their precision synthesis and fundamental mechanistic studies remain a challenge. The vast colloidal synthesis and processing parameters of LHP NCs in combination with the batch‐to‐batch and lab‐to‐lab variation problems further complicate their progress. In response, a self‐driving fluidic micro‐processor is presented for accelerated navigation through the complex synthesis and processing parameter space of NCs with multistage chemistries. The capability of the developed autonomous experimentation strategy is demonstrated for a time‐, material‐, and labor‐efficient search through the sequential halide exchange and cation doping reactions of LHP NCs. Next, a machine learning model of the modular fluidic micro‐processors is autonomously built for accelerated fundamental studies of the in‐flow metal cation doping of LHP NCs. The surrogate model of the sequential halide exchange and cation doping reactions of LHP NCs is then utilized for five closed‐loop synthesis campaigns with different target NC doping levels. The precise and intelligent NC synthesis and processing strategy, presented herein, can be further applied toward the autonomous discovery and development of novel impurity‐doped NCs with applications in next‐generation energy technologies.

Recently, slug-flow crystallizers (SFCs) have been proposed for continuous manufacturing of colloidal quantum dots (QDs). Despite the intriguing advantages of SFCs for controlled manufacturing of QDs, it has been difficult to account for the wide crystal size distribution (CSD) caused by slug-to-slug (S2S) variation, and the absence of a modeling and control framework made it challenging to fine-tune the QD size distribution. In response, we developed a computational fluid dynamics (CFD) model to simulate the S2S variation in SFCs. The results from the CFD model were integrated with a slug crystallizer model, which can describe the effect of S2S heterogeneity on crystallization of QDs in SFCs. Specifically, the slug crystallizer model was constructed by combining a continuum model with a kinetic Monte Carlo model. Based on the proposed CFD-based multiscale model, an optimal operation problem was formulated to ensure a good set-point (QD size) tracking performance and a narrow CSD.

Recently, continuous production of perovskite quantum dots (QDs) has received substantial attention, and has also posed certain engineering challenges. Specifically, an absence of well established crystallization models, lack of batch-to-continuous scale up studies, and unavailability of set-point tracking platforms are the major roadblocks. In this work, we tackle these challenges by (a) proposing a first-principled kinetic Monte Carlo (kMC) model to describe crystallization kinetics, (b) constructing a multiscale model to design a slug flow crystallizer (SFC) for mass production of QDs, and (c) formulating an optimization problem for optimal operation of the SFC. Furthermore, the complex fluid dynamics in a SFC was modeled using ANSYS Fluent and was integrated with a continuum crystallization model, which has not been addressed by the previous studies. In the optimal operation problem, an artificial neural network (ANN) based surrogate model was coupled with the multiscale model for better computational efficiency, and to ensure a good set-point tracking performance.

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 a wide range of emerging materials. However, as the material syntheses become more complex, the meta-decisions of artificial intelligence (AI)-guided decision-making algorithms used in autonomous platforms become more important. In this work, a surrogate model is developed using data from over 1000 in-house conducted syntheses of metal halide perovskite quantum dots in a self-driven modular microfluidic material synthesizer. The model is designed to represent the global failure rate, unfeasible regions of the synthesis space, synthesis ground truth, and sampling noise of a real robotic material synthesis system with multiple output parameters (peak emission, emission linewidth, and quantum yield). With this model, over 150 AI-guided decision-making strategies within a single-period horizon reinforcement learning framework are automatically explored across more than 600 000 simulated experiments - the equivalent of 7.5 years of continuous robotic operation and 400 L of reagents - to identify the most effective methods for accelerated materials development with multiple objectives. Specifically, the structure and meta-decisions of an ensemble neural network-based material development strategy are investigated, which offers a favorable technique for intelligently and efficiently navigating a complex material synthesis space with multiple targets. The developed ensemble neural network-based decision-making algorithm enables more efficient material formulation optimization in a no prior information environment than well-established algorithms.

Among all-inorganic metal halide perovskite quantum dots (PQDs), cesium lead chloride (CsPbCl3) with its large band-gap energy is an excellent candidate for enhancement of PQD radiative pathways through incorporation of additional internal energy transfer within its exciton band gap. In this study, we introduce a post-synthetic chemistry for ultrafast metal cation doping of CsPbCl3 QDs with a high degree of tunability, using a model transition metal impurity dopant, manganese. Due to the fast nature of the post-synthetic metal cation-doping reaction, an engineered time-to-space transformation strategy is employed to unravel the kinetics and fundamental mechanism of the doping process. Using a modular microfluidic platform equipped with a translational in situ absorption and photoluminescence spectroscopy probe, we propose a heterogeneous surface-doping mechanism through a vacancy-assisted metal cation migration. The developed in-flow doping strategy can open new avenues for on-demand optoelectronic properties tuning and scalable precision synthesis of high-quality metal cation-doped PQDs.

A four-phase segmented flow regime for continuous biphasic reaction processes is introduced, characterized over 1500 automatically conducted experiments, and used for biphasic ligand exchange of CdSe quantum dots.

Autonomous experimentation and chemical discovery strategies are rapidly rising across multiple fields of science. However, closed-loop material development approaches have not been widely employed in colloidal nanoscience mainly due to the challenges in synthesis space size, sensitivity to reaction conditions, and the complexity of monitoring multiple synthesis outputs. Recent advancements in automated reactor designs for controlled and reproducible nanocrystal synthesis and intelligent experiment selection algorithms are leading to wider propagation of artificial intelligence-guided autonomous experimentation techniques in colloidal nanoscience. This review will cover the current literature on closed-loop, autonomous platforms for accelerated development of colloidal nanomaterials and discuss the critical features and strategies for developing autonomous robotic experimentation systems suitable to problems in colloidal nanoscience, while providing the context, effectiveness, and prospects of each technique. Then, we will discuss some immediate opportunities in the field for more rapid technological advancement and colloidal nanomaterial discovery.

Inorganic lead halide perovskite quantum dots (QDs) have emerged as a promising semiconducting nanomaterial candidate for widespread applications, including next-generation solar cells, displays, and photocatalysts. The optoelectronic properties of colloidal QDs are majorly dictated by their bandgap energy (related to their size). Thus, it is important to fine-tune the size while having fast and continuous production of QDs. However, the mass and heat transfer limitations of batch reactors with batch-to-batch variations have hindered precise control over the size-dependent optoelectronic properties of QDs. Thus, to address this knowledge gap, we propose a multiscale model for continuous flow manufacturing of colloidal perovskite QDs. Specifically, a first-principled kinetic Monte Carlo model is integrated with a continuum model to describe a plug-flow crystallizer (PFC). The PFC has two manipulated inputs, precursor concentration and superficial flow velocity, to fine-tune the size of QDs. Furthermore, a neural network based surrogate model is designed to identify an optimal input trajectory which will ensure that the desired QD size is achieved, thereby taking a step towards controlled and reliable nanomanufacturing of QDs.

Self-driving laboratories are quickly growing in capability, making research in the exploration of advanced functional materials and molecules on the edge of a new era of productivity. As researchers near the widespread adoption of these powerful tools, we must assess their trajectory and the impact of their future developments.

Identifying the optimal formulation of emerging inorganic lead halide perovskite quantum dots (LHP QDs) with their vast colloidal synthesis universe and multiple synthesis/postsynthesis processing parameters is a challenging undertaking for material‐ and time‐intensive, batch synthesis strategies. Herein, a modular microfluidic synthesis strategy, integrated with an artificial intelligence (AI)‐guided decision‐making agent for intelligent navigation through the complex colloidal synthesis universe of LHP QDs with 10 individually controlled synthesis parameters and an accessible parameter space exceeding 2 × 107, is introduced. Utilizing the developed autonomous microfluidic experimentation strategy within a global learning framework, the optimal formulation of LHP QDs is rapidly identified through a two‐step colloidal synthesis and postsynthesis halide exchange reaction, for 10 different emission colors in less than 40 min per desired peak emission energy. Using two in‐series microfluidic reactors enables continuous bandgap engineering of LHP QDs via in‐line halide exchange reactions without the need for an intermediate washing step. Using an inert gas within a three‐phase flow format enables successful, self‐synchronized continuous delivery of halide salt precursor into moving droplets containing LHP QDs, resulting in accelerated closed‐loop formulation optimization and end‐to‐end continuous manufacturing of LHP QDs with desired optoelectronic properties.

Green solvent utilization and recovery enabled by switchable hydrophilicity solvents (SHSs), using carbon dioxide as the switching trigger, offer intriguing advantages in sustainable chemistry. To further elevate SHSs, an intensified continuous flow strategy is presented, providing an accurate in situ reaction monitoring and a scalable green solvent extraction route.

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, an Artificial Chemist is presented: the integration of machine-learning-based experiment selection and high-efficiency autonomous flow chemistry. With the self-driving Artificial Chemist, made-to-measure inorganic perovskite quantum dots (QDs) in flow are autonomously synthesized, and their quantum yield and composition polydispersity at target bandgaps, spanning 1.9 to 2.9 eV, are simultaneously tuned. 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, the Artificial Chemist is pre-trained to use a new batch of precursors and further accelerate the synthetic path discovery of QD compositions, by at least twofold. 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 peak emission energy.