An autonomous organic reaction search engine for chemical reactivity

Dragone, Vincenza; Sans, Víctor; Henson, Alon; Granda, Jarosław M.; Cronin, Leroy

doi:10.1038/ncomms15733

Cited by 87 publications

(74 citation statements)

References 41 publications

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“…6). Several groups have demonstrated this closed-loop approach in a range of applications, including carbon nanotubes, Bose-Einstein condensates, alloys, substituted functional organic molecules, oil droplets and the search for new chemical reactions [23][24][25][26]57,[282][283][284] . A notable example is the development of the Autonomous Research System, ARES 23 , to optimize the synthesis of carbon nanotubes.…”

Section: Autonomous Experimentationmentioning

confidence: 99%

Accelerating the discovery of materials for clean energy in the era of smart automation

et al. 2018

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| The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry , materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery , which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace. volume 3 | mAY 2018 | 5 PERSPECTIVES

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Section: Autonomous Experimentationmentioning

confidence: 99%

Accelerating the discovery of materials for clean energy in the era of smart automation

et al. 2018

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“…There are many approaches to automated yield optimization, some of which are described below. As optimization of reaction conditions requires live feedback from the robotic system, many different detectors have been introduced to monitor progress of the reactions, including benchtop nuclear magnetic resonance spectroscopy [2], infrared spectroscopy [11], mass spectrometry [12], Raman spectroscopy [13], UV-Vis spectroscopy [14], and high-performance liquid chromatography [15]. Harvested data are then fed to optimization algorithms to explore the often multidimensional parameter space.…”

Section: Robotics For Automation and Optimization In Chemistrymentioning

confidence: 99%

“…We recently demonstrated a flow system for navigating a network of organic reactions utilizing an infrared spectrometer as the sensor for data feedback. The system was able to select the most reactive starting materials autonomously on the basis of change in the infrared spectra between starting materials and products [11]. Building on that work, we built a robotic platform for autonomous searching of chemical space with three benchtop analytical instruments (infrared spectroscopy, nuclear magnetic resonance spectrometry, and mass spectrometry) for on-line analysis.…”

Section: Chemistry and Discovery Via Programmable Modular System: 'Thmentioning

confidence: 99%

Universal Chemical Synthesis and Discovery with ‘The Chemputer’

Gromski

Granda

Cronin

2020

Trends in Chemistry

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117

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There is a growing drive in the chemistry community to exploit rapidly growing robotic technologies along with artificial intelligence-based approaches. Applying this to chemistry requires a holistic approach to chemical synthesis design and execution. Here, we outline a universal approach to this problem beginning with an abstract representation of the practice of chemical synthesis that then informs the programming and automation required for its practical realization. Using this foundation to construct closed-loop robotic chemical search engines, we can generate new discoveries that may be verified, optimized, and repeated entirely automatically. These robots can perform chemical reactions and analyses much faster than can be done manually. As such, this leads to a road map whereby molecules can be discovered, optimized, and made on demand from a digital code. Automation in Chemical SynthesisMethodologies for the automation of chemical synthesis, optimization, and discovery have not generally been designed for the realities of laboratory-based research, tending instead to focus on engineering solutions to practical problems. We argue that the potential of rapidly developing technologies (e.g., machine learning and robotics) are more fully realized by operating seamlessly with the way that synthetic chemists currently work ( Figure 1) [1]. This is because the organic chemist often works by thinking backwards as much as they do forwards when planning a synthetic procedure. To reproduce this fundamental mode of operation, a new universal approach to the automated exploration of chemical space is needed that combines an abstraction of chemical synthesis with robotic hardware and closed-loop programming [2,3]. However, this leads chemists to constantly test the reactions with different synthetic parameters and conditions. The alternative to this problem, as shown in this opinion article, is the development of an approach to universal chemistry using a programming language with automation in combination with machine learning and artificial intelligence (AI).Chemists already benefit from algorithms in the field of chemometrics and, therefore, automation is one step forward that might help chemists to navigate and search chemical space more quickly, efficiently, and importantly, without bias. Chemometrics is a field that employs a broad range of algorithms to solve chemistry-related problems and has been well established over the past 50 years [4]. Figure 2 presents a standard chemometrics workflow for processing data. The process begins with data that may be of various formats that depend upon the experiment type and/or posed question. The next step is data preprocessing, which covers a variety of procedures depending on the type of data analyzed (e.g., peak detection, input of missing data, and/or normalization). This process is followed by statistical modeling, which is divided into supervised and unsupervised approaches. Probably one of the most well-known unsupervised approaches is principal component analysis (s...

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“…The machine‐learning system was able to predict the reactivity of about 1000 reaction combinations with accuracy higher than 80%. A similar approach demonstrated the design of an autonomous reaction network that included the evaluation of the reactions' outcome by in‐line spectroscopy and real‐time feedback and analysis with an algorithm that is able to distinguish and select the most reactive pathways. Machine learning was proven to be very suitable to allow alternatives to human experience and expertise for a fast suggestion of a possible reaction outcome or the suggestion of the right starting materials and conditions for reaction design and retrosynthesis.…”

Section: Automated Autonomous Synthesis For Organic Chemistrymentioning

confidence: 99%

Miniaturized and Automated Synthesis of Biomolecules—Overview and Perspectives

et al. 2019

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Chemical synthesis is performed by reacting different chemical building blocks with defined stoichiometry, while meeting additional conditions, such as temperature and reaction time. Such a procedure is especially suited for automation and miniaturization. Life sciences lead the way to synthesizing millions of different oligonucleotides in extremely miniaturized reaction sites, e.g., pinpointing active genes in whole genomes, while chemistry advances different types of automation. Recent progress in matrix‐assisted laser desorption/ionization mass spectrometry (MALDI‐MS) imaging could match miniaturized chemical synthesis with a powerful analytical tool to validate the outcome of many different synthesis pathways beyond applications in the life sciences. Thereby, due to the radical miniaturization of chemical synthesis, thousands of molecules can be synthesized. This in turn should allow ambitious research, e.g., finding novel synthesis routes or directly screening for photocatalysts. Herein, different technologies are discussed that might be involved in this endeavor. A special emphasis is given to the obstacles that need to be tackled when depositing tiny amounts of materials to many different extremely miniaturized reaction sites.

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An autonomous organic reaction search engine for chemical reactivity

Cited by 87 publications

References 41 publications

Accelerating the discovery of materials for clean energy in the era of smart automation

Accelerating the discovery of materials for clean energy in the era of smart automation

Universal Chemical Synthesis and Discovery with ‘The Chemputer’

Miniaturized and Automated Synthesis of Biomolecules—Overview and Perspectives

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