A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis

Journal of the American Chemical Society, Год журнала: 2023, Номер 145(23), С. 12870 - 12883

Опубликована: Июнь 2, 2023

The development of chiral catalysts that can provide high enantioselectivities across a wide assortment substrates or reaction range is priority for many catalyst design efforts. While several approaches are available to aid in the identification general systems, there currently no simple procedure directly measuring how given could be. Herein, we present catalyst-agnostic workflow centered on unsupervised machine learning enables rapid assessment and quantification generality. uses curated literature data sets descriptors visualize cluster chemical space coverage. This network then be applied derive generality metric through designer equations interfaced with other regression techniques prediction. As validating case studies, have successfully this method identify-through-quantification most chemotype an organocatalytic asymmetric Mannich predicted phosphoric acid addition nucleophiles imines. mechanistic basis gleaned from calculated values by deconstructing contributions enantiomeric excess overall result. Finally, our permitted mechanistically informative screening allow experimentalists rationally select highest probability achieving good result first round development. Overall, findings represent framework interrogating generality, strategy should relevant catalytic systems widely synthesis.

Язык: Английский

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Rapid planning and analysis of high-throughput experiment arrays for reaction discovery

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Июль 3, 2023

High-throughput experimentation (HTE) is an increasingly important tool in reaction discovery. While the hardware for running HTE chemical laboratory has evolved significantly recent years, there remains a need software solutions to navigate data-rich experiments. Here we have developed phactor™, that facilitates performance and analysis of laboratory. phactor™ allows experimentalists rapidly design arrays reactions or direct-to-biology experiments 24, 96, 384, 1,536 wellplates. Users can access online reagent data, such as inventory, virtually populate wells with produce instructions perform array manually, assistance liquid handling robot. After completion array, analytical results be uploaded facile evaluation, guide next series All metadata, are stored machine-readable formats readily translatable various software. We also demonstrate use discovery several chemistries, including identification low micromolar inhibitor SARS-CoV-2 main protease. Furthermore, been made available free academic 24- 96-well via interface.

Язык: Английский

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A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0

Processes, Год журнала: 2023, Номер 11(2), С. 330 - 330

Опубликована: Янв. 19, 2023

With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems industrial chemistry and chemical engineering. Therefore, this review provides an overview application AI techniques, particular machine learning, design, synthesis, process optimization over past years. In review, focus on structure-function relationship analysis, synthetic route planning, automated synthesis. Finally, we discuss challenges future making products.

Язык: Английский

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ChemOS 2.0: an orchestration architecture for chemical self-driving laboratories

Опубликована: Авг. 7, 2023

Self-driving laboratories (SDLs), which combine automated experimental hardware with computational experiment planning, have emerged as powerful tools for accelerating materials discovery. The intrinsic complexity created by their multitude of components requires an effective orchestration platform to ensure the correct operation diverse setups. Existing frameworks, however, are either tailored specific setups or not been implemented real-world synthesis. To address these issues, we introduce ChemOS 2.0, architecture that efficiently coordinates communication, data exchange, and instruction management among modular laboratory components. By treating "operating system" 2.0 combines ab-initio calculations, statistical algorithms guide closed-loop operations. demonstrate its capabilities, showcase in a case study focused on discovering organic laser molecules. results confirm 2.0's prowess research potential valuable design future SDL platforms.

Язык: Английский

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The Electrolab: An open-source, modular platform for automated characterization of redox-active electrolytes

Device, Год журнала: 2023, Номер 1(5), С. 100103 - 100103

Опубликована: Окт. 10, 2023

Electrochemical characterization of redox-active molecules in solution requires exploration manifold conditions (e.g., concentration, electrolyte type, pH, ionic strength), leading to tedious and time-consuming experiments that are prone user error. Here, we introduce the Electrolab, a modular, automated electrochemical platform seamlessly interfaces with common laboratory instrumentation low-cost electromechanical components. We integrated gantry-type robot carrying multipurpose nozzle assembly dispense mix solutions as well degas clean cell containing multiplexed microelectrochemical arrays. The system operates using Python code universal Arduino-based controller. demonstrate Electrolab by autonomously analyzing redox mediator performing 200 voltammograms data analysis steps across range conditions. In addition, is used titrate polymer identify for optimizing performance. Overall, device enables high-throughput, systematic electrolytes, opening new avenues closed-loop optimization.

Язык: Английский

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