Automated Library Generation and Serendipity Quantification Enables Diverse Discovery in Coordination Chemistry

Kowalski, Daniel J.; MacGregor, Catriona M; Long, De‐Liang; Bell, Nicola L.; Cronin, Leroy

doi:10.1021/jacs.2c11066

Cited by 8 publications

(8 citation statements)

References 77 publications

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“…Many other projects have been completed or are nearing completion using systems based on this architecture. For example, in 2021/22 the MWP was used as an effective tool for the multistep synthesis of lanthanide-based Mo-POMs with interesting magnetic properties and for the discovery of several new coordination compounds . The system’s capabilities continue to increase with each project and iteration.…”

Section: Modular Platformsmentioning

confidence: 99%

Robotic Modules for the Programmable Chemputation of Molecules and Materials

et al. 2023

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Before leveraging big data methods like machine learning and artificial intelligence (AI) in chemistry, there is an imperative need for an affordable, universal digitization standard. This mirrors the foundational requisites of the digital revolution, which demanded standard architectures with precise specifications. Recently, we have developed automated platforms tailored for chemical AI-driven exploration, including the synthesis of molecules, materials, nanomaterials, and formulations. Our focus has been on designing and constructing affordable standard hardware and software modules that serve as a blueprint for chemistry digitization across varied fields. Our platforms can be categorized into four types based on their applications: (i) discovery systems for the exploration of chemical space and novel reactivity, (ii) systems for the synthesis and manufacture of fine chemicals, (iii) platforms for formulation discovery and exploration, and (iv) systems for materials discovery and synthesis. We also highlight the convergent evolution of these platforms through shared hardware, firmware, and software alongside the creation of a unique programming language for chemical and material systems. This programming approach is essential for reliable synthesis, designing experiments, discovery, optimization, and establishing new collaboration standards. Furthermore, it is crucial for verifying literature findings, enhancing experimental outcome reliability, and fostering collaboration and sharing of unsuccessful experiments across different research labs.

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Section: Modular Platformsmentioning

confidence: 99%

Robotic Modules for the Programmable Chemputation of Molecules and Materials

et al. 2023

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“…For example, in 2021/22 the MWP was used as an effective tool for the multistep synthesis of lanthanide-based Mo-POMs with interesting magnetic properties 53 and for the discovery of several new coordination compounds. 54 The system's capabilities are continuing to increase with each project and iteration.…”

Section: Modular Platformsmentioning

confidence: 99%

Robotic Modules for the Programmable Chemputation of Molecules and Materials

Cronin

Salley

Manzano

et al. 2023

Preprint

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Before big data methods like machine learning and artificial intelligence methods can be used in chemistry, the digitization of chemistry and materials requires the development of a universal standard that is both affordable and broadly applicable. This has parallels with the foundations of the digital revolution which required standard architectures with a well-defined specification. Recently, we have developed automated platforms for the chemical artificial intelligence driven discovery, synthesis of molecules, materials, nanomaterials, and formulations. To avoid the use of highly expensive systems we focussed on the design and construction of standard hardware and software modules creating a road map for the digitization of chemistry across different fields. Our platforms can be divided into four different categories depending on their application: i) discovery systems for the search of chemical space and new reactivity, ii) synthesis and manufacture of fine chemicals, iii) formulation discovery and exploration, and iv) materials discovery and synthesis. We describe the evolution and convergence of these platforms in terms of, common hardware, firmware, and software along with the development of a programming language for chemical and material systems. This programming approach is not only useful for reliable synthesis, but for design of experiments, discovery, optimisation and providing new standards for collaboration. This approach is also vital for the verification of findings published in the literature, databases, to increase the reliability of experimental outcomes, and to allow collaboration across different research laboratory settings as well as the sharing of failed experiments.

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“…These synthesis inputs were selected to provide a high-dimensional chemical space to explore. [28][29][30][31][32] The electrode holder was designed to hold three electrodes in the middle of reactor vials without being in contact with each other. The holder was mounted on an XZ motor to move the electrodes between the sample to analyse on the chemical reaction module or the cleaning station and lower the electrodes into the sample or cleaning/storage solutions.…”

Section: Ecarus Workflowmentioning

confidence: 99%

“…The platform forms complexes by selecting from a maximum of four possible ligands combining with two metal salts or solely investigating the combinations of three metal oxide salts, thus demonstrating the robust and versatile chemistry the platform can perform. These synthesis inputs were selected to provide a high‐dimensional chemical space to explore [28–32] …”

Section: Introductionmentioning

confidence: 99%

An Autonomous Electrochemical Discovery Robot that Utilises Probabilistic Algorithms: Probing the Redox Behaviour of Inorganic Materials

Laws,

Tze‐Kiat Ng,

Sharma

et al. 2023

ChemElectroChem

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The discovery of new electroactive materials is slow due to the large combinatorial chemical space of possible experiments. Efficient exploration of redox‐active chemical space requires a machine learning assisted robotic platform with real‐time feedback. Here, we developed a closed‐loop robotic platform which is capable of synthesis and electrochemical characterisation controlled using a probabilistic algorithm. This was used to probe the redox behaviour of different polyoxometalates (POMs) precursors and explore the formation of redox‐active coordination complexes. The system can run accurate analytical electrochemical measurements whilst maintaining the performance and accuracy of both the working and reference electrodes. The platform successfully ran and analysed 336 coordination chemistry reactions by performing ca. 2500 cyclic voltammetry (CV) scans for analysis and electrode cleaning. Overall, the platform carried out over 9900 operations in 350 hours at a rate of 28 operations per hour, and we identified 24 complex solutions which showed significantly different redox activity. Experiments were performed using a universal chemical synthesis language (χDL) with variable inputs. The platform was used autonomously to investigate a range of POM precursor materials demonstrating 45 % increase in capacitance. The experiments ran for 36 hours with more than 6400 operations during which we analysed 200 POM precursor solutions.

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Automated Library Generation and Serendipity Quantification Enables Diverse Discovery in Coordination Chemistry

Cited by 8 publications

References 77 publications

Robotic Modules for the Programmable Chemputation of Molecules and Materials

Robotic Modules for the Programmable Chemputation of Molecules and Materials

Robotic Modules for the Programmable Chemputation of Molecules and Materials

An Autonomous Electrochemical Discovery Robot that Utilises Probabilistic Algorithms: Probing the Redox Behaviour of Inorganic Materials

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