Chemical process optimization by computer — a self-directed chemical synthesis system

Winicov, H.; Schainbaum, Julius; Buckley, J.; Longino, G.; Hill, Jan; Berkoff, Charles E.

doi:10.1016/s0003-2670(01)83110-x

Cited by 46 publications

(44 citation statements)

References 8 publications

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“…To the best of our knowledge, the first self‐driving laboratory reported an autonomous chemical synthesis where all the steps of the experimental procedure are controlled by a computer and driven by a local optimization algorithm 17. Since then, the self‐driving approach was used to optimize carbon nanotube growth,18 polyoxometalate clusters, metal‐based alloys,19 and organic synthesis reactions 20,21.…”

Section: Figurementioning

confidence: 99%

Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems

et al. 2020

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Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends, which represents a clear trend toward multicomponent active layer blends. The development of high‐throughput and autonomous experimentation methods is reported for the effective optimization of multicomponent polymer blends for OPVs. A method for automated film formation enabling the fabrication of up to 6048 films per day is introduced. Equipping this automated experimentation platform with a Bayesian optimization, a self‐driving laboratory is constructed that autonomously evaluates measurements to design and execute the next experiments. To demonstrate the potential of these methods, a 4D parameter space of quaternary OPV blends is mapped and optimized for photostability. While with conventional approaches, roughly 100 mg of material would be necessary, the robot‐based platform can screen 2000 combinations with less than 10 mg, and machine‐learning‐enabled autonomous experimentation identifies stable compositions with less than 1 mg.

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Section: Figurementioning

confidence: 99%

Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems

et al. 2020

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“…Eine Serie von Verbesserungen in der kontinuierlichen Reaktionsverfolgung,d er Computer-assistierten Selbstoptimierung und den Algorithmen fürm aschinelles Lernen und künstliche Intelligenz konnte parallel erreicht werden und hat das Potenzial, sowohl die kundenorientierte als auch die generelle Entwicklung neuer automatisierter Synthesen voranzutreiben. [111] Die komplette Handhabung eines biologischen Systems durch einen Roboter konnte bereits demonstriert werden (Abbildung 5). Der Roboterforscher "Adam" kann selbstständig Hypothesen über den Zusammenhang von Genfunktionen in der Hefe Saccharomyces cerevisiae generieren und diese durch automatisierte Experimente testen.…”

Section: Automatisierung Der Reaktionsoptimierung Des Molekularen Deunclassified

Die molekulare industrielle Revolution: zur automatisierten Synthese organischer Verbindungen

Trobe

Burke

2018

Angewandte Chemie

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Derzeitkündigt sichein Wandel von der manuellen Synthese spezifischer Moleküle,die über individuell angepasste Routen verläuft, hin zu automatisch zusammenfügten Verbindungen unterschiedlichster Klassen mit nur einem Knopfdruckan. Die Entwicklung einer Maschine fürdie Herstellung unterschiedlichsterorganischer Moleküle je nachBedarf ist eine komplexe Aufgabe,e ss ind aber bereits bedeutende Fortschritte auf Basis von zwei komplementären Ansätzen zu verzeichnen:1 )Automatisierung individueller Routen fürdie Synthese diverser Verbindungen, wobei viele unterschiedliche Reaktionstypen und spezifische Reagentien genutzt werden kçnnen, und 2) Automatisierung einer generellen Plattform, die die Herstellung unterschiedlicher Verbindungen ausgehend von diversen Bausteinen, die mit derselben Kupplungsreaktion verknüpft werden, ermçglicht. Fortschritte in diesen Bereichen bieten die Mçglichkeit, Synthese-Engpässe zu vermeiden und den Fokus molekularer Innovation auf die Entwicklung neuer Methoden zu lenken und so eine "molekulare industrielle Revolution" zu beschleunigen.

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“…The . . control strategy employed by these systems progressed from hard-wired logic control (Legrand & Foucard, 1978), to microcomputer-based automation (Winicov et al, 1978), to laboratory robot controlled organic synthesis (Frisbee et aL, 1984). Development of these systems was motivated primarily by the need to optimize synthetic yields in a synthesis containing several important controllable parameters.…”

Section: Automation Of Radiotracer Synthesismentioning

confidence: 99%

“…', optimization algorithm to results obtained from computer and robot controlled experiments (Winicov et al, 1978;Frisbee et al, 1984).…”

Section: Automation Of Radiotracer Synthesismentioning

confidence: 99%

Automation for the Synthesis and Application of PET Radiopharmaceuticals

Alexoff

2002

Handbook of Radiopharmaceuticals

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These unique constraints include short synthesis (often limited to20r3 half-lives) times and control from behind bulky shielding structures that make both access to, and visibility of, radiochemical processes and equipment difficult. Often Curie levels of positron emitting nuclides are required for synthesis of PET radiopharmaceuticals, making this potentially dangerous for a radiochemist or laboratory specialist. The use of short half-lived radionuclides also necessitates that many PET radiotracers (particularly those labeled with 1lC, *3N, and '50) be synthesized repetitively during the day, each dose being produced separately just before administration.Radiotracer synthesis must be reliable and efficient to keep the costs of PET procedures down. Furthermore, radiotracer synthesis procedures for human use must produce pharmaceutical quality products and be well documented and controlled to help satisfy requirements of federal and local regulations on human research.Automation can help PET research institutions overcome all of these potential '" limitations. A look at the history of the development of successful automated PET radiotracer synthesis machines reveals a richness in engineering solutions to these problems that still exists today.

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Chemical process optimization by computer — a self-directed chemical synthesis system

Cited by 46 publications

References 8 publications

Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems

Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems

Die molekulare industrielle Revolution: zur automatisierten Synthese organischer Verbindungen

Automation for the Synthesis and Application of PET Radiopharmaceuticals

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