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Tom G, Schmid SP, Baird SG, Cao Y, Darvish K, Hao H, Lo S, Pablo-García S, Rajaonson EM, Skreta M, Yoshikawa N, Corapi S, Akkoc GD, Strieth-Kalthoff F, Seifrid M, Aspuru-Guzik A. Self-Driving Laboratories for Chemistry and Materials Science. Chem Rev 2024. [PMID: 39137296 DOI: 10.1021/acs.chemrev.4c00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.
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Affiliation(s)
- Gary Tom
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Stefan P Schmid
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, CH-8093 Zurich, Switzerland
| | - Sterling G Baird
- Acceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Yang Cao
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Acceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Kourosh Darvish
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
- Acceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Han Hao
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Acceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Stanley Lo
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Sergio Pablo-García
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
| | - Ella M Rajaonson
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Marta Skreta
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Naruki Yoshikawa
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Samantha Corapi
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Gun Deniz Akkoc
- Forschungszentrum Jülich GmbH, Helmholtz Institute for Renewable Energy Erlangen-Nürnberg, Cauerstr. 1, 91058 Erlangen, Germany
- Department of Chemical and Biological Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Egerlandstr. 3, 91058 Erlangen, Germany
| | - Felix Strieth-Kalthoff
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- School of Mathematics and Natural Sciences, University of Wuppertal, Gaußstraße 20, 42119 Wuppertal, Germany
| | - Martin Seifrid
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Department of Materials Science and Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States of America
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
- Acceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
- Department of Materials Science & Engineering, University of Toronto, Toronto, Ontario M5S 3E4, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 661 University Ave, Toronto, Ontario M5G 1M1, Canada
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Tan JD, Ramalingam B, Wong SL, Cheng JJW, Lim YF, Chellappan V, Khan SA, Kumar J, Hippalgaonkar K. Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization. J Chem Inf Model 2023; 63:4560-4573. [PMID: 37432764 DOI: 10.1021/acs.jcim.3c00504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
The skew and shape of the molecular weight distribution (MWD) of polymers have a significant impact on polymer physical properties. Standard summary metrics statistically derived from the MWD only provide an incomplete picture of the polymer MWD. Machine learning (ML) methods coupled with high-throughput experimentation (HTE) could potentially allow for the prediction of the entire polymer MWD without information loss. In our work, we demonstrate a computer-controlled HTE platform that is able to run up to 8 unique variable conditions in parallel for the free radical polymerization of styrene. The segmented-flow HTE system was equipped with an inline Raman spectrometer and offline size exclusion chromatography (SEC) to obtain time-dependent conversion and MWD, respectively. Using ML forward models, we first predict monomer conversion, intrinsically learning varying polymerization kinetics that change for each experimental condition. In addition, we predict entire MWDs including the skew and shape as well as SHAP analysis to interpret the dependence on reagent concentrations and reaction time. We then used a transfer learning approach to use the data from our high-throughput flow reactor to predict batch polymerization MWDs with only three additional data points. Overall, we demonstrate that the combination of HTE and ML provides a high level of predictive accuracy in determining polymerization outcomes. Transfer learning can allow exploration outside existing parameter spaces efficiently, providing polymer chemists with the ability to target the synthesis of polymers with desired properties.
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Affiliation(s)
- Jin Da Tan
- Institute of Materials Research & Engineering, Agency for Science Technology and Research, 2 Fusionopolis Way, 138634 Singapore, Singapore
- National University of Singapore Graduate School - Integrative Sciences and Engineering Programme, 21 Lower Kent Ridge Road, Singapore 119077, Singapore
| | - Balamurugan Ramalingam
- Institute of Materials Research & Engineering, Agency for Science Technology and Research, 2 Fusionopolis Way, 138634 Singapore, Singapore
- Institute of Sustainability for Chemicals, Energy and Environment, Agency for Science Technology and Research, 8 Biomedical Grove, Singapore 138665, Singapore
| | - Swee Liang Wong
- Institute of Materials Research & Engineering, Agency for Science Technology and Research, 2 Fusionopolis Way, 138634 Singapore, Singapore
- Home Team Science and Technology Agency, Singapore 138507, Singapore
| | - Jayce Jian Wei Cheng
- Institute of Materials Research & Engineering, Agency for Science Technology and Research, 2 Fusionopolis Way, 138634 Singapore, Singapore
| | - Yee-Fun Lim
- Institute of Materials Research & Engineering, Agency for Science Technology and Research, 2 Fusionopolis Way, 138634 Singapore, Singapore
- Institute of Sustainability for Chemicals, Energy and Environment, Agency for Science Technology and Research, 8 Biomedical Grove, Singapore 138665, Singapore
| | - Vijila Chellappan
- Institute of Materials Research & Engineering, Agency for Science Technology and Research, 2 Fusionopolis Way, 138634 Singapore, Singapore
| | - Saif A Khan
- National University of Singapore Graduate School - Integrative Sciences and Engineering Programme, 21 Lower Kent Ridge Road, Singapore 119077, Singapore
- Department of Chemical and Biomolecular Engineering - National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore
| | - Jatin Kumar
- Institute of Materials Research & Engineering, Agency for Science Technology and Research, 2 Fusionopolis Way, 138634 Singapore, Singapore
- Xinterra Pte. Ltd., 77 Robinson Road, Singapore 068896, Singapore
| | - Kedar Hippalgaonkar
- Institute of Materials Research & Engineering, Agency for Science Technology and Research, 2 Fusionopolis Way, 138634 Singapore, Singapore
- Department of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
- Institute of Functional Intelligent Materials - National University of Singapore, 4 Science Drive 2, Singapore 117544, Singapore
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3
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Kathiravelu P, Arnold M, Fleischer J, Yao Y, Awasthi S, Goel AK, Branen A, Sarikhani P, Kumar G, Kothare MV, Mahmoudi B. CONTROL-CORE: A Framework for Simulation and Design of Closed-Loop Peripheral Neuromodulation Control Systems. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2022; 10:36268-36285. [PMID: 36199437 PMCID: PMC9531851 DOI: 10.1109/access.2022.3161471] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Closed-loop Vagus Nerve Stimulation (VNS) based on physiological feedback signals is a promising approach to regulate organ functions and develop therapeutic devices. Designing closed-loop neurostimulation systems requires simulation environments and computing infrastructures that support i) modeling the physiological responses of organs under neuromodulation, also known as physiological models, and ii) the interaction between the physiological models and the neuromodulation control algorithms. However, existing simulation platforms do not support closed-loop VNS control systems modeling without extensive rewriting of computer code and manual deployment and configuration of programs. The CONTROL-CORE project aims to develop a flexible software platform for designing and implementing closed-loop VNS systems. This paper proposes the software architecture and the elements of the CONTROL-CORE platform that allow the interaction between a controller and a physiological model in feedback. CONTROL-CORE facilitates modular simulation and deployment of closed-loop peripheral neuromodulation control systems, spanning multiple organizations securely and concurrently. CONTROL-CORE allows simulations to run on different operating systems, be developed in various programming languages (such as Matlab, Python, C++, and Verilog), and be run locally, in containers, and in a distributed fashion. The CONTROL-CORE platform allows users to create tools and testbenches to facilitate sophisticated simulation experiments. We tested the CONTROL-CORE platform in the context of closed-loop control of cardiac physiological models, including pulsatile and nonpulsatile rat models. These were tested using various controllers such as Model Predictive Control and Long-Short-Term Memory based controllers. Our wide range of use cases and evaluations show the performance, flexibility, and usability of the CONTROL-CORE platform.
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Affiliation(s)
| | - Mark Arnold
- Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Jake Fleischer
- Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Yuyu Yao
- Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Shubham Awasthi
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India
| | - Aviral Kumar Goel
- Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, K. K. Birla Goa Campus, Sancoale, Goa 403726, India
| | - Andrew Branen
- Department of Chemical and Materials Engineering, San José State University, San Jose, CA 95192, USA
| | - Parisa Sarikhani
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA
| | - Gautam Kumar
- Department of Chemical and Materials Engineering, San José State University, San Jose, CA 95192, USA
| | - Mayuresh V Kothare
- Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Babak Mahmoudi
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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4
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Vanpoucke DE, Delgove MA, Stouten J, Noordijk J, De Vos N, Matthysen K, Deroover GG, Mehrkanoon S, Bernaerts KV. A machine learning approach for the design of hyperbranched polymeric dispersing agents based on aliphatic polyesters for radiation curable inks. POLYM INT 2022. [DOI: 10.1002/pi.6378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Danny E.P. Vanpoucke
- Maastricht University, Aachen‐Maastricht Institute for Biobased Materials (AMIBM), Brightlands Chemelot campus, Urmonderbaan 22, 6167 RD Geleen the Netherlands
| | - Marie A.F. Delgove
- Maastricht University, Aachen‐Maastricht Institute for Biobased Materials (AMIBM), Brightlands Chemelot campus, Urmonderbaan 22, 6167 RD Geleen the Netherlands
| | - Jules Stouten
- Maastricht University, Aachen‐Maastricht Institute for Biobased Materials (AMIBM), Brightlands Chemelot campus, Urmonderbaan 22, 6167 RD Geleen the Netherlands
| | - Jurrie Noordijk
- Maastricht University, Aachen‐Maastricht Institute for Biobased Materials (AMIBM), Brightlands Chemelot campus, Urmonderbaan 22, 6167 RD Geleen the Netherlands
| | - Nils De Vos
- ChemStream, Drie Eikenstraat 661, B‐2650 Edegem Belgium
| | | | | | - Siamak Mehrkanoon
- Maastricht University, Department of Data Science and Knowledge Engineering, Paul‐Henri Spaaklaan 1, 6229 EN Maastricht the Netherlands
| | - Katrien V. Bernaerts
- Maastricht University, Aachen‐Maastricht Institute for Biobased Materials (AMIBM), Brightlands Chemelot campus, Urmonderbaan 22, 6167 RD Geleen the Netherlands
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5
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Hao Z, Zhang C, Lapkin AA. Efficient Surrogates Construction of Chemical Processes: Case studies on Pressure Swing Adsorption and
Gas‐to‐Liquids. AIChE J 2022. [DOI: 10.1002/aic.17616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Zhimian Hao
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
| | - Chonghuan Zhang
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
| | - Alexei A. Lapkin
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd Singapore
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6
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Knox ST, Parkinson SJ, Wilding CYP, Bourne R, Warren NJ. Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation. Polym Chem 2022. [DOI: 10.1039/d2py00040g] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Application of artificial intelligence and machine learning for polymer discovery offers an opportunity to meet the drastic need for the next generation high performing and sustainable polymer materials. Here, these...
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7
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Fong AY, Pellouchoud L, Davidson M, Walroth RC, Church C, Tcareva E, Wu L, Peterson K, Meredig B, Tassone CJ. Utilization of machine learning to accelerate colloidal synthesis and discovery. J Chem Phys 2021; 154:224201. [PMID: 34241189 DOI: 10.1063/5.0047385] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work presents a closed-loop machine learning-based strategy for colloidal synthesis of nanoparticles, assuming no prior knowledge of the synthetic process, in order to show that synthetic discovery can be accelerated despite limited data availability.
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Affiliation(s)
- Anthony Y Fong
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - Lenson Pellouchoud
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | | | - Richard C Walroth
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - Carena Church
- Citrine Informatics, Redwood City, California 94063, USA
| | - Ekaterina Tcareva
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - Liheng Wu
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - Kyle Peterson
- Citrine Informatics, Redwood City, California 94063, USA
| | - Bryce Meredig
- Citrine Informatics, Redwood City, California 94063, USA
| | - Christopher J Tassone
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
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8
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Cao L, Russo D, Lapkin AA. Automated robotic platforms in design and development of formulations. AIChE J 2021. [DOI: 10.1002/aic.17248] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Liwei Cao
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. Singapore
| | - Danilo Russo
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
| | - Alexei A. Lapkin
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. Singapore
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9
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Zhang C, Amar Y, Cao L, Lapkin AA. Solvent Selection for Mitsunobu Reaction Driven by an Active Learning Surrogate Model. Org Process Res Dev 2020. [DOI: 10.1021/acs.oprd.0c00376] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Chonghuan Zhang
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Yehia Amar
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Liwei Cao
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd., 1 CREATE Way, CREATE Tower #05-05, 138602 Singapore
| | - Alexei A. Lapkin
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd., 1 CREATE Way, CREATE Tower #05-05, 138602 Singapore
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10
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Coley CW, Eyke NS, Jensen KF. Autonomous Discovery in the Chemical Sciences Part I: Progress. Angew Chem Int Ed Engl 2020; 59:22858-22893. [DOI: 10.1002/anie.201909987] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Indexed: 01/05/2023]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
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11
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Coley CW, Eyke NS, Jensen KF. Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.201909987] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
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12
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Dong Y, Georgakis C, Mustakis J, Han L, McMullen JP. Optimization of pharmaceutical reactions using the dynamic response surface methodology. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106778] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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13
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Self-optimising reactive extractions: towards the efficient development of multi-step continuous flow processes. J Flow Chem 2020. [DOI: 10.1007/s41981-020-00086-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
AbstractDownstream purification of products and intermediates is essential for the development of continuous flow processes. Described herein, is a study on the use of a modular and reconfigurable continuous flow platform for the self-optimisation of reactive extractions and multi-step reaction-extraction processes. The selective extraction of one amine from a mixture of two similar amines was achieved with an optimum separation of 90%, and in this case, the black-box optimisation approach was superior to global polynomial modelling. Furthermore, this methodology was utilised to simultaneously optimise the continuous flow synthesis and work-up of N-benzyl-α-methylbenzylamine with respect to four variables, resulting in a significantly improved purity.
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14
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Cova TFGG, Pais AACC. Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns. Front Chem 2019; 7:809. [PMID: 32039134 PMCID: PMC6988795 DOI: 10.3389/fchem.2019.00809] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 11/11/2019] [Indexed: 12/14/2022] Open
Abstract
Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.
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Affiliation(s)
- Tânia F. G. G. Cova
- Coimbra Chemistry Centre, CQC, Department of Chemistry, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
| | - Alberto A. C. C. Pais
- Coimbra Chemistry Centre, CQC, Department of Chemistry, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
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15
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Aka EC, Wimmer E, Barré E, Vasudevan N, Cortés-Borda D, Ekou T, Ekou L, Rodriguez-Zubiri M, Felpin FX. Reconfigurable Flow Platform for Automated Reagent Screening and Autonomous Optimization for Bioinspired Lignans Synthesis. J Org Chem 2019; 84:14101-14112. [PMID: 31568728 DOI: 10.1021/acs.joc.9b02263] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Naturally occurring benzoxanthenones, which belong to the vast family of lignans, are promising biologically relevant targets. They are biosynthetically produced by the oxidative dimerization of 2-propenyl phenols. In this manuscript, we disclose a powerful automated flow-based strategy for identifying and optimizing a cobalt-catalyzed oxidizing system for the bioinspired dimerization of 2-propenyl phenols. We designed a reconfigurable flow reactor associating online monitoring and process control instrumentation. Our machine was first configured as an automated screening platform to evaluate a matrix of 4 catalysts (plus the blank) and 5 oxidants (plus the blank) at two different temperatures, resulting in an array of 50 reactions. The automated screening was conducted on micromole scale at a rate of one fully characterized reaction every 26 min. After having identified the most promising cobalt-catalyzed oxidizing system, the automated screening platform was straightforwardly reconfigured to an autonomous self-optimizing flow reactor by implementation of an optimization algorithm in the closed-loop system. The optimization campaign allowed the determination of very effective experimental conditions in a limited number of experiments, which allowed us to prepare the natural products carpanone and polemannone B as well as synthetic analogues.
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Affiliation(s)
- Ehu Camille Aka
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Eric Wimmer
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Elvina Barré
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Natarajan Vasudevan
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Daniel Cortés-Borda
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Tchirioua Ekou
- Université Nangui Abrogoua , Laboratoire de Thermodynamique et de Physico-Chimie du Milieu , 02 BP 801 Abidjan 02 , Côte d'Ivoire
| | - Lynda Ekou
- Université Nangui Abrogoua , Laboratoire de Thermodynamique et de Physico-Chimie du Milieu , 02 BP 801 Abidjan 02 , Côte d'Ivoire
| | - Mireia Rodriguez-Zubiri
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - François-Xavier Felpin
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
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16
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Fujinami M, Seino J, Nukazawa T, Ishida S, Iwamoto T, Nakai H. Virtual Reaction Condition Optimization based on Machine Learning for a Small Number of Experiments in High-dimensional Continuous and Discrete Variables. CHEM LETT 2019. [DOI: 10.1246/cl.190267] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Mikito Fujinami
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Junji Seino
- Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Takumi Nukazawa
- Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Shintaro Ishida
- Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Takeaki Iwamoto
- Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Hiromi Nakai
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Element Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Katsura, Kyoto 615-8520, Japan
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17
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Dimitrov T, Kreisbeck C, Becker JS, Aspuru-Guzik A, Saikin SK. Autonomous Molecular Design: Then and Now. ACS APPLIED MATERIALS & INTERFACES 2019; 11:24825-24836. [PMID: 30908004 DOI: 10.1021/acsami.9b01226] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
The success of deep machine learning in processing of large amounts of data, for example, in image or voice recognition and generation, raises the possibilities that these tools can also be applied for solving complex problems in materials science. In this forum article, we focus on molecular design that aims to answer the question on how we can predict and synthesize molecules with tailored physical, chemical, or biological properties. A potential answer to this question could be found by using intelligent systems that integrate physical models and computational machine learning techniques with automated synthesis and characterization tools. Such systems learn through every single experiment in an analogy to a human scientific expert. While the general idea of an autonomous system for molecular synthesis and characterization has been around for a while, its implementations for the materials sciences are sparse. Here we provide an overview of the developments in chemistry automation and the applications of machine learning techniques in the chemical and pharmaceutical industries with a focus on the novel capabilities that deep learning brings in.
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Affiliation(s)
- Tanja Dimitrov
- Kebotix, Inc. , 501 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States
| | - Christoph Kreisbeck
- Kebotix, Inc. , 501 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States
- Department of Chemistry and Chemical Biology , Harvard University , Cambridge , Massachusetts 02138 , United States
| | - Jill S Becker
- Kebotix, Inc. , 501 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States
| | - Alán Aspuru-Guzik
- Kebotix, Inc. , 501 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States
- Department of Chemistry and Department of Computer Science , University of Toronto , Toronto , Ontario M5S 3H6 , Canada
| | - Semion K Saikin
- Kebotix, Inc. , 501 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States
- Department of Chemistry and Chemical Biology , Harvard University , Cambridge , Massachusetts 02138 , United States
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18
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Waldron C, Pankajakshan A, Quaglio M, Cao E, Galvanin F, Gavriilidis A. An autonomous microreactor platform for the rapid identification of kinetic models. REACT CHEM ENG 2019. [DOI: 10.1039/c8re00345a] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Rapid estimation of kinetic parameters with high precision is facilitated by automation combined with online Model-Based Design of Experiments.
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Affiliation(s)
- Conor Waldron
- Dept of Chemical Engineering
- University College London
- London
- UK
| | | | - Marco Quaglio
- Dept of Chemical Engineering
- University College London
- London
- UK
| | - Enhong Cao
- Dept of Chemical Engineering
- University College London
- London
- UK
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19
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Clayton AD, Manson JA, Taylor CJ, Chamberlain TW, Taylor BA, Clemens G, Bourne RA. Algorithms for the self-optimisation of chemical reactions. REACT CHEM ENG 2019. [DOI: 10.1039/c9re00209j] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Self-optimising chemical systems have experienced a growing momentum in recent years. Herein, we review algorithms used for the self-optimisation of chemical reactions in an accessible way for the general chemist.
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Affiliation(s)
- Adam D. Clayton
- Institute of Process Research and Development
- School of Chemistry & School of Chemical and Process Engineering
- University of Leeds
- UK
| | - Jamie A. Manson
- Institute of Process Research and Development
- School of Chemistry & School of Chemical and Process Engineering
- University of Leeds
- UK
| | - Connor J. Taylor
- Institute of Process Research and Development
- School of Chemistry & School of Chemical and Process Engineering
- University of Leeds
- UK
| | - Thomas W. Chamberlain
- Institute of Process Research and Development
- School of Chemistry & School of Chemical and Process Engineering
- University of Leeds
- UK
| | | | | | - Richard A. Bourne
- Institute of Process Research and Development
- School of Chemistry & School of Chemical and Process Engineering
- University of Leeds
- UK
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20
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Cortés-Borda D, Wimmer E, Gouilleux B, Barré E, Oger N, Goulamaly L, Peault L, Charrier B, Truchet C, Giraudeau P, Rodriguez-Zubiri M, Le Grognec E, Felpin FX. An Autonomous Self-Optimizing Flow Reactor for the Synthesis of Natural Product Carpanone. J Org Chem 2018; 83:14286-14299. [DOI: 10.1021/acs.joc.8b01821] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Daniel Cortés-Borda
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Eric Wimmer
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Boris Gouilleux
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Elvina Barré
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Nicolas Oger
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Lubna Goulamaly
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Louis Peault
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Benoît Charrier
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Charlotte Truchet
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6241, LINA, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Patrick Giraudeau
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
- Institut Universitaire de France, 1 rue Descartes, 75231 Paris Cedex 05, France
| | - Mireia Rodriguez-Zubiri
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Erwan Le Grognec
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - François-Xavier Felpin
- Université de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
- Institut Universitaire de France, 1 rue Descartes, 75231 Paris Cedex 05, France
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21
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22
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Hsieh HW, Coley CW, Baumgartner LM, Jensen KF, Robinson RI. Photoredox Iridium–Nickel Dual-Catalyzed Decarboxylative Arylation Cross-Coupling: From Batch to Continuous Flow via Self-Optimizing Segmented Flow Reactor. Org Process Res Dev 2018. [DOI: 10.1021/acs.oprd.8b00018] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Hsiao-Wu Hsieh
- Global Discovery Chemistry − Chemical Technology Group, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Connor W. Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Lorenz M. Baumgartner
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Klavs F. Jensen
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Richard I. Robinson
- Global Discovery Chemistry − Chemical Technology Group, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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23
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Baumgartner LM, Coley CW, Reizman BJ, Gao KW, Jensen KF. Optimum catalyst selection over continuous and discrete process variables with a single droplet microfluidic reaction platform. REACT CHEM ENG 2018. [DOI: 10.1039/c8re00032h] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
A mixed-integer nonlinear program (MINLP) algorithm to optimize catalyst turnover number (TON) and product yield by simultaneously modulating discrete variables—catalyst types—and continuous variables—temperature, residence time, and catalyst loading—was implemented and validated.
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Affiliation(s)
| | - Connor W. Coley
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
| | - Brandon J. Reizman
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
| | - Kevin W. Gao
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
- Department of Chemical and Biomolecular Engineering
| | - Klavs F. Jensen
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
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24
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McAfee T, Montgomery RD, Zekoski T, Wu A, Reed WF. Automatic, simultaneous control of polymer composition and molecular weight during free radical copolymer synthesis. POLYMER 2018. [DOI: 10.1016/j.polymer.2017.12.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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25
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Echtermeyer A, Amar Y, Zakrzewski J, Lapkin A. Self-optimisation and model-based design of experiments for developing a C-H activation flow process. Beilstein J Org Chem 2017; 13:150-163. [PMID: 28228856 PMCID: PMC5301945 DOI: 10.3762/bjoc.13.18] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 01/05/2017] [Indexed: 12/18/2022] Open
Abstract
A recently described C(sp3)-H activation reaction to synthesise aziridines was used as a model reaction to demonstrate the methodology of developing a process model using model-based design of experiments (MBDoE) and self-optimisation approaches in flow. The two approaches are compared in terms of experimental efficiency. The self-optimisation approach required the least number of experiments to reach the specified objectives of cost and product yield, whereas the MBDoE approach enabled a rapid generation of a process model.
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Affiliation(s)
- Alexander Echtermeyer
- Aachener Verfahrenstechnik – Process Systems Engineering, RWTH Aachen University, Aachen, Germany
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Yehia Amar
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Jacek Zakrzewski
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Alexei Lapkin
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
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26
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Cortés-Borda D, Kutonova KV, Jamet C, Trusova ME, Zammattio F, Truchet C, Rodriguez-Zubiri M, Felpin FX. Optimizing the Heck–Matsuda Reaction in Flow with a Constraint-Adapted Direct Search Algorithm. Org Process Res Dev 2016. [DOI: 10.1021/acs.oprd.6b00310] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Daniel Cortés-Borda
- Université
de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6241, LINA, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
- Université
de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Ksenia V. Kutonova
- Department
of Biotechnology and Organic Chemistry, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia
| | - Corentin Jamet
- Université
de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6241, LINA, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Marina E. Trusova
- Department
of Biotechnology and Organic Chemistry, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia
| | - Françoise Zammattio
- Université
de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6241, LINA, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Charlotte Truchet
- Université
de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6230, CEISAM, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - Mireia Rodriguez-Zubiri
- Université
de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6241, LINA, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
| | - François-Xavier Felpin
- Université
de Nantes, UFR des Sciences et des Techniques, CNRS UMR 6241, LINA, 2 rue de la Houssinière, 44322 Nantes Cedex 3, France
- Institut Universitaire
de France, 1 rue Descartes, 75231 Paris Cedex 05, France
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27
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McAfee T, Leonardi N, Montgomery R, Siqueira J, Zekoski T, Drenski MF, Reed WF. Automatic Control of Polymer Molecular Weight during Synthesis. Macromolecules 2016. [DOI: 10.1021/acs.macromol.6b01522] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Terry McAfee
- Tulane University, New Orleans, Louisiana 70118, United States
| | - Natalie Leonardi
- Advanced Polymer
Monitoring Technologies, Inc., New Orleans, Louisiana 70125, United States
| | - Rick Montgomery
- Advanced Polymer
Monitoring Technologies, Inc., New Orleans, Louisiana 70125, United States
| | - Julia Siqueira
- Tulane University, New Orleans, Louisiana 70118, United States
| | - Thomas Zekoski
- Tulane University, New Orleans, Louisiana 70118, United States
| | - Michael F. Drenski
- Advanced Polymer
Monitoring Technologies, Inc., New Orleans, Louisiana 70125, United States
| | - Wayne F. Reed
- Tulane University, New Orleans, Louisiana 70118, United States
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28
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Sans V, Cronin L. Towards dial-a-molecule by integrating continuous flow, analytics and self-optimisation. Chem Soc Rev 2016; 45:2032-43. [PMID: 26815081 PMCID: PMC6057606 DOI: 10.1039/c5cs00793c] [Citation(s) in RCA: 125] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The employment of continuous-flow platforms for synthetic chemistry is becoming increasingly popular in research and industrial environments. Integrating analytics in-line enables obtaining a large amount of information in real-time about the reaction progress, catalytic activity and stability, etc. Furthermore, it is possible to influence the reaction progress and selectivity via manual or automated feedback optimisation, thus constituting a dial-a-molecule approach employing digital synthesis. This contribution gives an overview of the most significant contributions in the field to date.
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Affiliation(s)
- Victor Sans
- Department of Chemical and Environmental Engineering, University of Nottingham, NG7 2RD, UK.
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