Delocalized, asynchronous, closed-loop discovery of organic laser emitters.

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enabled delocalized and asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based artificial intelligence experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing-and democratizing-scientific discovery.

Dynamic sampling in autonomous process optimization.

Autonomous process optimization (APO) is a technology that has recently found utility in a multitude of process optimization challenges. In contrast to most APO examples in microflow reactor systems, we recently presented a system capable of optimization in high-throughput batch reactor systems. The drawback of APO in a high-throughput batch reactor system is the reliance on reaction sampling at a predetermined static timepoint rather than a dynamic endpoint. Static timepoint sampling can lead to the inconsistent capture of the process performance under each process parameter permutation. This is important because critical process behaviors such as rate acceleration accompanied by decomposition could be missed entirely. To address this drawback, we implemented a dynamic reaction endpoint determination strategy to capture the product purity once the process stream stabilized. We accomplished this through the incorporation of a real-time plateau detection algorithm into the APO workflow to measure and report the product purity at the dynamically determined reaction endpoint. We then applied this strategy to the autonomous optimization of a photobromination reaction towards the synthesis of a pharmaceutically relevant intermediate. In doing so, we not only uncovered process conditions to access the desired monohalogenation product in 85 UPLC area % purity with minimal decomposition risk, but also measured the effect of each parameter on the process performance. Our results highlight the advantage of incorporating dynamic sampling in APO workflows to drive optimization toward a stable and high-performing process.

Keeping an "eye" on the experiment: computer vision for real-time monitoring and control.

This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical outputs (e.g., liquid level, homogeneity, turbidity, solid, residue, and color), offering a method for rapid data acquisition and deeper analysis from multiple visual cues. We demonstrate a single platform (consisting of CV, machine learning, real-time monitoring techniques, and flexible hardware) to monitor and control vision-based experimental techniques, including solvent exchange distillation, antisolvent crystallization, evaporative crystallization, cooling crystallization, solid-liquid mixing, and liquid-liquid extraction. Both qualitative (video capturing) and quantitative data (visual outputs measurement) were obtained which provided a method for data cross-validation. Our CV model's ease of use, generalizability, and non-invasiveness make it an appealing complementary option to in situ and real-time analytical monitoring tools and mathematical modeling. Additionally, our platform is integrated with Mettler-Toledo's iControl software, which acts as a centralized system for real-time data collection, visualization, and storage. With consistent data representation and infrastructure, we were able to efficiently transfer the technology and reproduce results between different labs. This ability to easily monitor and respond to the dynamic situational changes of the experiments is pivotal to enabling future flexible automation workflows.

Accelerated chemical science with AI.

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.

Reaction monitoring via benchtop nuclear magnetic resonance spectroscopy: A practical comparison of on-line stopped-flow and continuous-flow sampling methods.

The ability for nuclear magnetic resonance (NMR) spectroscopy to provide quantitative, structurally rich information makes this spectroscopic technique an attractive reaction monitoring tool. The practicality of NMR for this type of analysis has only increased in the recent years with the influx of commercially available benchtop NMR instruments and compatible flow systems. In this study, we aim to compare 19F NMR reaction profiles acquired under both on-line continuous-flow and stopped-flow sampling methods, with modern benchtop NMR instrumentation, and two reaction systems: a homogeneous imination reaction and a biphasic activation of a carboxylic acid to acyl fluoride. Reaction trends with higher data density can be acquired with on-line continuous-flow analyses, and this work highlights that representative reaction trends can be acquired without any correction when monitoring resonances with a shorter spin-lattice relaxation time (T1), and with the used flow conditions. On-line stopped-flow analyses resulted in representative reaction trends in all cases, including the monitoring of resonances with a long T1, without the need of any correction factors. The benefit of easier data analysis, however, comes with the cost of time, as the fresh reaction solution must be flowed into the NMR system, halted, and time must be provided for spins to become polarized in the instrument's external magnetic field prior to spectral measurement. Results for one of the reactions were additionally compared with the use of a high-field NMR.

A reliable external calibration method for reaction monitoring with benchtop NMR.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical technique with the ability to acquire both quantitative and structurally insightful data for multiple components in a test sample. This makes NMR spectroscopy a desirable tool to understand, monitor, and optimize chemical transformations. While quantitative NMR (qNMR) approaches relying on internal standards are well-established, using an absolute external calibration scheme is beneficial for reaction monitoring as resonance overlap complications from an added reference material to the sample can be avoided. Particularly, this type of qNMR technique is of interest with benchtop NMR spectrometers as the likelihood of resonance overlap is only enhanced with the lower magnetic field strengths of the used permanent magnets. The included study describes a simple yet robust methodology to determine concentration conversion factors for NMR systems using single- and multi-analyte linear regression models. This approach is leveraged to investigate a pharmaceutically relevant amide coupling batch reaction. An on-line stopped-flow (i.e., interrupted-flow or paused-flow) benchtop NMR system was used to monitor both the 1,1'-carbonyldiimidazole (CDI) promoted acid activation and the amide coupling. The results highlight how quantitative measurements in benchtop NMR systems can provide valuable information and enable analysts to make decisions in real time.

Insights into the synthesis of NHC-stabilized Au nanoclusters through real-time reaction monitoring.

Atomically precise gold nanoclusters (AuNCs) are interesting nanomaterials with potential applications in catalysis, bioimaging and optoelectronics. Their compositions and properties are commonly evaluated by various analytical techniques, including UV-vis spectroscopy, NMR spectroscopy, ESI mass spectrometry, and single-crystal X-ray diffraction. While these techniques have provided detailed insights into the structure and properties of nanoclusters, synthetic methods still suffer from a lack of in situ and real-time reaction monitoring methodologies. This limits insight into the mechanism of formation of AuNCs and hinders attempts at optimization. We have demonstrated the utility of HPLC-MS as a monitoring methodology in the synthesis of two NHC-protected gold nanoclusters: [Au13(NHC)9Cl3]2+ and [Au24(NHC)14Cl2H3]3+. Herein we show that HPLC coupled with mass spectrometry and 13C NMR spectroscopy of labelled derivatives enables new insight into critical reaction dynamics of AuNCs synthesis and rapid reaction optimization.

A Method for Converting HPLC Peak Area from Online Reaction Monitoring to Concentration Using Nonlinear Regression.

Online HPLC reaction progress monitoring provides detailed data-rich profiles; however, extracting kinetic information requires ultraviolet-visible response factors to determine concentrations from peak areas. If the reaction's overall mass balance is known and some analytical trend for all relevant species can be recorded, it is possible to estimate the absolute response factors of all species using a system of linear equations. We delineate a method using the Microsoft Solver plug-in to convert time course profiles to reagent concentrations without analytical standards.

A Materials Acceleration Platform for Organic Laser Discovery.

Conventional materials discovery is a laborious and time-consuming process that can take decades from initial conception of the material to commercialization. Recent developments in materials acceleration platforms promise to accelerate materials discovery using automation of experiments coupled with machine learning. However, most of the automation efforts in chemistry focus on synthesis and compound identification, with integrated target property characterization receiving less attention. In this work, an automated platform is introduced for the discovery of molecules as gain mediums for organic semiconductor lasers, a problem that has been challenging for conventional approaches. This platform encompasses automated lego-like synthesis, product identification, and optical characterization that can be executed in a fully integrated end-to-end fashion. Using this workflow to screen organic laser candidates, discovered eight potential candidates for organic lasers is discovered. The lasing threshold of four molecules in thin-film devices and find two molecules with state-of-the-art performance is tested. These promising results show the potential of automated synthesis and screening for accelerated materials development.

Enantioselective synthesis of (-)-tetrabenazine via continuous crystallization-induced diastereomer transformation.

A multi-well continuous CIDT approach with inline racemization of the solution phase is presented. Using two in-house built PATs and a flow reactor, we were able to successfully crystallize an enantiopure salt of TBZ, the active metabolite of the tardive dyskinesia drug valbenazine. Despite discovering an undesired racemic solid phase, inline racemization combined with careful control of crystallization conditions allowed for multigram quantities of enantiopure material to be harvested using our setup. Critically, this control was made possible by the use of PATs to observe and quantify the composition of both the solid and solution phases.

Ring walking as a regioselectivity control element in Pd-catalyzed C-N cross-coupling.

Ring walking is an important mechanistic phenomenon leveraged in many catalytic C-C bond forming reactions. However, ring walking has been scarcely studied under Buchwald-Hartwig amination conditions despite the importance of such transformations. An in-depth mechanistic study of the Buchwald-Hartwig amination is presented focussing on ligand effects on ring walking behavior. The ability of palladium catalysts to promote or inhibit ring walking is strongly influenced by the chelating nature of the ligand. In stark contrast, the resting state of the catalyst had no impact on ring walking behavior. Furthermore, the complexity of the targeted system enabled the differentiation between catalysts which undergo ring walking versus diffusion-controlled coupling. The insights gained in this study were leveraged to achieve desymmetrization of a tetrabrominated precursor. A small library of asymmetric 2,2',7,7'-tetrakis[N,N-di(4-methoxyphenyl)amino]-9,9'spirobifluorene (SpiroOMeTAD) derivatives were successfully synthesized using this strategy highlighting the ease with which libraries of these compounds can be accessed for screening.

A survey of substrate specificity among Auxiliary Activity Family 5 copper radical oxidases.

There is significant contemporary interest in the application of enzymes to replace or augment chemical reagents toward the development of more environmentally sound and sustainable processes. In particular, copper radical oxidases (CRO) from Auxiliary Activity Family 5 Subfamily 2 (AA5_2) are attractive, organic cofactor-free catalysts for the chemoselective oxidation of alcohols to the corresponding aldehydes. These enzymes were first defined by the archetypal galactose-6-oxidase (GalOx, EC 1.1.3.13) from the fungus Fusarium graminearum. The recent discovery of specific alcohol oxidases (EC 1.1.3.7) and aryl alcohol oxidases (EC 1.1.3.47) within AA5_2 has indicated a potentially broad substrate scope among fungal CROs. However, only relatively few AA5_2 members have been characterized to date. Guided by sequence similarity network and phylogenetic analysis, twelve AA5_2 homologs have been recombinantly produced and biochemically characterized in the present study. As defined by their predominant activities, these comprise four galactose 6-oxidases, two raffinose oxidases, four broad-specificity primary alcohol oxidases, and two non-carbohydrate alcohol oxidases. Of particular relevance to applications in biomass valorization, detailed product analysis revealed that two CROs produce the bioplastics monomer furan-2,5-dicarboxylic acid (FDCA) directly from 5-hydroxymethylfurfural (HMF). Furthermore, several CROs could desymmetrize glycerol (a by-product of the biodiesel industry) to D- or L-glyceraldehyde. This study furthers our understanding of CROs by doubling the number of characterized AA5_2 members, which may find future applications as biocatalysts in diverse processes.

Mechanistic Investigation of Castagnoli-Cushman Multicomponent Reactions Leading to a Three-Component Synthesis of Dihydroisoquinolones.

The mechanisms for the three- and four-component variants of the Castagnoli-Cushman reaction (CCR) have been investigated. A series of crossover experiments were conducted to probe the structure and reactivity of known amide-acid intermediates for the three- and four-component variants of the CCR (3CR and 4CR, respectively). Control experiments paired with in situ reaction monitoring with infrared spectroscopy for the 4CR align with a mechanism in which amide-acids derived from maleic anhydride can reversibly form free amine and cyclic anhydride. Although this equilibrium is unfavorable, the aldehyde present can trap the primary amine through imine formation and react with the enol form of the anhydride through a Mannich-like mechanism. This detailed mechanistic investigation coupled with additional crossover experiments supports an analogous mechanism for the 3CR and has led to the elucidation of new 3CR conditions with homophthalic anhydride, amines, and aldehydes for the formation of dihydroisoquinolones in good yields and excellent diastereoselectivity. This work represents the culmination of more than a decade of mechanistic speculation for the 3- and 4CR, enabling the design of new multicomponent reactions that exploit this novel mechanism.

Real-Time Monitoring of Solid-Liquid Slurries: Optimized Synthesis of Tetrabenazine.

Solid-liquid slurries are vital and increasingly prevalent in the pharmaceutical and chemical industries. Despite the importance of these heterogeneous systems, process control and optimization are fundamentally hindered by a lack of compatible real-time analytical techniques. We present herein an online HPLC monitoring platform enabling access to real-time compositional information on slurries. We demonstrate the system by investigating the heterogeneous synthesis reaction of tetrabenazine. Furthermore, we integrated our online HPLC platform with the orthogonal monitoring techniques of a pH probe and a microscopic imaging probe to provide additional mechanistic insight. These combined insights enable the optimization of tetrabenazine synthesis in terms of reaction time, byproduct formation, and diastereomeric purity of the final product.

Two Fusarium copper radical oxidases with high activity on aryl alcohols.

BACKGROUND: Biomass valorization has been suggested as a sustainable alternative to petroleum-based energy and commodities. In this context, the copper radical oxidases (CROs) from Auxiliary Activity Family 5/Subfamily 2 (AA5_2) are attractive biocatalysts for the selective oxidation of primary alcohols to aldehydes. Originally defined by the archetypal galactose 6-oxidase from Fusarium graminearum, fungal AA5_2 members have recently been shown to comprise a wide range of specificities for aromatic, aliphatic and furan-based alcohols. This suggests a broader substrate scope of native CROs for applications. However, only 10% of the annotated AA5_2 members have been characterized to date. RESULTS: Here, we define two homologues from the filamentous fungi Fusarium graminearum and F. oxysporum as predominant aryl alcohol oxidases (AAOs) through recombinant production in Pichia pastoris, detailed kinetic characterization, and enzyme product analysis. Despite possessing generally similar active-site architectures to the archetypal FgrGalOx, FgrAAO and FoxAAO have weak activity on carbohydrates, but instead efficiently oxidize specific aryl alcohols. Notably, both FgrAAO and FoxAAO oxidize hydroxymethyl furfural (HMF) directly to 5-formyl-2-furoic acid (FFCA), and desymmetrize the bioproduct glycerol to the uncommon L-isomer of glyceraldehyde. CONCLUSIONS: This work expands understanding of the catalytic diversity of CRO from AA5_2 to include unique representatives from Fusarium species that depart from the well-known galactose 6-oxidase activity of this family. Detailed enzymological analysis highlights the potential biotechnological applications of these orthologs in the production of renewable plastic polymer precursors and other chemicals.

Automated solubility screening platform using computer vision.

Solubility screening is an essential, routine process that is often labor intensive. Robotic platforms have been developed to automate some aspects of the manual labor involved. However, many of the existing systems rely on traditional analytic techniques such as high-performance liquid chromatography, which require pre-calibration for each compound and can be resource consuming. In addition, automation is not typically end-to-end, requiring user intervention to move vials, establish analytical methods for each compound and interpret the raw data. We developed a closed-loop, flexible robotic system with integrated solid and liquid dosing capabilities that relies on computer vision and iterative feedback to successfully measure caffeine solubility in multiple solvents. After initial researcher input (<2 min), the system ran autonomously, screening five different solvent systems (20-80 min each). The resulting solubility values matched those obtained using traditional manual techniques.

Automated Experimentation Powers Data Science in Chemistry.

Data science has revolutionized chemical research and continues to break down barriers with new interdisciplinary studies. The introduction of computational models and machine learning (ML) algorithms in combination with automation and traditional experimental techniques has enabled scientific advancement across nearly every discipline of chemistry, from materials discovery, to process optimization, to synthesis planning. However, predictive tools powered by data science are only as good as their data sets and, currently, many of the data sets used to train models suffer from several limitations, including being sparse, limited in scope and requiring human curation. Likewise, computational data faces limitations in terms of accurate modeling of nonideal systems and can suffer from low translation fidelity from simulation to real conditions. The lack of diverse data and the need to be able to test it experimentally reduces both the accuracy and scope of the predictive models derived from data science. This Account contextualizes the need for more complex and diverse experimental data and highlights how the seamless integration of robotics, machine learning, and data-rich monitoring techniques can be used to access it with minimal human labor.We propose three broad categories of data in chemistry: data on fundamental properties, data on reaction outcomes, and data on reaction mechanics. We highlight flexible, automated platforms that can be deployed to acquire and leverage these data. The first platform combines solid- and liquid-dosing modules with computer vision to automate solubility screening, thereby gathering fundamental data that are necessary for almost every experimental design. Using computer vision offers the additional benefit of creating a visual record, which can be referenced and used to further interrogate and gain insight on the data collected. The second platform iteratively tests reaction variables proposed by a ML algorithm in a closed-loop fashion. Experimental data related to reaction outcomes are fed back into the algorithm to drive the discovery and optimization of new materials and chemical processes. The third platform uses automated process analytical technology to gather real-time data related to reaction kinetics. This system allows the researcher to directly interrogate the reaction mechanisms in granular detail to determine exactly how and why a reaction proceeds, thereby enabling reaction optimization and deployment.

Automation isn't automatic.

Automation has become an increasingly popular tool for synthetic chemists over the past decade. Recent advances in robotics and computer science have led to the emergence of automated systems that execute common laboratory procedures including parallel synthesis, reaction discovery, reaction optimization, time course studies, and crystallization development. While such systems offer many potential benefits, their implementation is rarely automatic due to the highly specialized nature of synthetic procedures. Each reaction category requires careful execution of a particular sequence of steps, the specifics of which change with different conditions and chemical systems. Careful assessment of these critical procedural requirements and identification of the tools suitable for effective experimental execution are key to developing effective automation workflows. Even then, it is often difficult to get all the components of an automated system integrated and operational. Data flows and specialized equipment present yet another level of challenge. Unfortunately, the pain points and process of implementing automated systems are often not shared or remain buried deep in the SI. This perspective provides an overview of the current state of automation of synthetic chemistry at the benchtop scale with a particular emphasis on core considerations and the ensuing challenges of deploying a system. Importantly, we aim to reframe automation as decidedly not automatic but rather an iterative process that involves a series of careful decisions (both human and computational) and constant adjustment.