3D designed and printed chemical generators for on demand reagent synthesis

Zalesskiy, Sergey S.; Kitson, Philip J.; Frei, Przemysław; Bubliauskas, Andrius; Cronin, Leroy

doi:10.1038/s41467-019-13328-6

Cited by 27 publications

(28 citation statements)

References 52 publications

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“…While synthetic approaches are relatively mature and the new vanguard is automating synthesis, new approaches are being developed that promise to dramatically improve the efficacy of synthesis, such as the "chemputer". [396,397] For simulation and analysis, there is greater scope for improving interoperability and veracity. Any computer model is necessarily a simplification of the experimental conditions and so integration of experimental results provides invaluable data to assess the robustness of the current model and refine it.…”

Section: Discussionmentioning

confidence: 99%

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High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

et al. 2020

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Notwithstanding these impediments, in many fields of materials science, solutions are being designed to mitigate hindrances to the efficient sampling of chemical space and improving the robustness of computational screening models through a combination of high throughput synthesis (HTS), characterization, and machine learning. In this review, we highlight key developments in high throughput approaches pertinent to porous materials. Specifically, we focus on developments in the field of zeolitic materials, metal-organic frameworks (MOFs), and touch upon covalent organic frameworks (COFs). Although superficially these classes of materials have little in common, there are structural links such as topology which allow for the consideration of how high throughput methods contrast with one another. By contrasting developments in different fields, we identify some underutilized approaches which could be leveraged in the future. We target structurally ordered materials because the exploration of chemical and topological space is more straightforward to enumerate, though similar approaches could be applied to disordered materials. The scale of the problem faced in materials science of targeted structure and function [1] is by no means unique; in drug discovery, HTS and chemoinformatic approaches have been used for more than 40 years in an attempt to accelerate the discovery of druggable molecules. [2,3] Unlike the drug discovery process, where Lipinski's "rule of 5" [4] provides a reliable top level sift for viable targets, identifying the most promising material for a target application requires very particular properties that may be intertwined in complex and contradictory ways. For example, thermoelectrics require high electrical conductivity and low thermal conductivity and yet these properties are typically correlated unless they can be separated. [5] Given the large scope of potential applications, the advent of the Materials Genome Initiative (MGI) [6,7] in the United States has undoubtedly helped to promote ways to solve the aforementioned obstacles and numerous others. Similarly there are major initiatives within the EU (e.g., NOMAD [8] and BIGmax [9]) and Switzerland (MARVEL [10]). In the MGI, nanoporous materials, including zeolites and MOFs, have been specifically targeted [11] and in this review, we seek to highlight particular challenges in the field of porous materials and how researchers have sought to overcome these challenges. We focus primarily on the methods used in HTS and rapid throughput/screening computational approaches. Aspects of high throughput computation applied to porous materials have been reviewed before, [12-14] as has high throughput experimentation (HTE) [15,16] but here we focus on their combination within an integrated workflow. Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre-and post-synthetic computer simulations are mostly carried out in a piecemeal a...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

et al. 2020

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“…Incorporation of digital tools into the workflow of chemistry has already begun to increase efficiency, discovery, and the pace of innovation and it is clear that the volume of scientific data will rapidly proliferate in the coming years. 9 , 19 − 23 In chemical synthesis and reaction analytics, it will be essential to capture all the key parameters of a preparation or experiment, including the context of the work. This approach will be essential for reproducibility, however, with only a few exceptions, this data has not been recorded reliably to date.…”

Section: Introductionmentioning

confidence: 99%

Chemputation and the Standardization of Chemical Informatics

Hammer

Leonov

Bell

et al. 2021

JACS Au

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The explosion in the use of machine learning for automated chemical reaction optimization is gathering pace. However, the lack of a standard architecture that connects the concept of chemical transformations universally to software and hardware provides a barrier to using the results of these optimizations and could cause the loss of relevant data and prevent reactions from being reproducible or unexpected findings verifiable or explainable. In this Perspective, we describe how the development of the field of digital chemistry or chemputation, that is the universal code-enabled control of chemical reactions using a standard language and ontology, will remove these barriers allowing users to focus on the chemistry and plug in algorithms according to the problem space to be explored or unit function to be optimized. We describe a standard hardware (the chemical processing programming architecture—the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability. Not only can a vast range of different chemistries be plugged into the hardware, but the ever-expanding developments in software and algorithms can also be accommodated. These technologies, when combined will allow chemistry, or chemputation, to follow computation—that is the running of code across many different types of capable hardware to get the same result every time with a low error rate.

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“… 12 This led to the creation of monolithic “chemical generators”, which allowed chemical products to be obtained on-demand with minimal human intervention to achieve the desired synthesis. 13 This approach was used to demonstrate reactionware’s applicability for the production of useful and high-value materials in good yields and purities. Reactionware bypasses traditional sophisticated manufacturing centers while also facilitating the translation of traditional glassware synthesis into a platform-independent digital code, creating digital blueprints that can be part of an online library of verified, synthesis-specific reactor designs available globally.…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of 3D-Printed Reactionware for Chemical Synthesis Digitization using ChemSCAD

et al. 2021

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We describe a system, ChemSCAD, for the creation of digital reactors based on the chemical operations, physical parameters, and synthetic sequence to produce a given target compound, to show that the system can translate the gram-scale batch synthesis of the antiviral compound Ribavirin (yield 43% over three steps), the narcolepsy drug Modafinil (yield 60% over three steps), and both batch and flow instances of the synthesis of the anticancer agent Lomustine (batch yield 65% over two steps) in purities greater than or equal to 96%. The syntheses of compounds developed using the ChemSCAD system, including reactor designs and analytical data, can be stored in a database repository, with the information necessary to critically evaluate and improve upon reactionware syntheses being easily shared and versioned.

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3D designed and printed chemical generators for on demand reagent synthesis

Cited by 27 publications

References 52 publications

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Chemputation and the Standardization of Chemical Informatics

Automatic Generation of 3D-Printed Reactionware for Chemical Synthesis Digitization using ChemSCAD

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