1
|
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.
Collapse
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
| |
Collapse
|
2
|
|
3
|
Ke J, Gao C, Folgueiras-Amador AA, Jolley KE, de Frutos O, Mateos C, Rincón JA, Brown RCD, Poliakoff M, George MW. Self-Optimization of Continuous Flow Electrochemical Synthesis Using Fourier Transform Infrared Spectroscopy and Gas Chromatography. APPLIED SPECTROSCOPY 2022; 76:38-50. [PMID: 34911387 DOI: 10.1177/00037028211059848] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A continuous-flow electrochemical synthesis platform has been developed to enable self-optimization of reaction conditions of organic electrochemical reactions using attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) and gas chromatography (GC) as online real-time monitoring techniques. We have overcome the challenges in using ATR FT-IR as the downstream analytical methods imposed when a large amount of hydrogen gas is produced from the counter electrode by designing two types of gas-liquid separators (GLS) for analysis of the product mixture flowing from the electrochemical reactor. In particular, we report an integrated GLS with an ATR FT-IR probe at the reactor outlet to give a facile and low-cost solution to determining the concentrations of products in gas-liquid two-phase flow. This approach provides a reliable method for quantifying low-volatile analytes, which can be problematic to be monitored by GC. Two electrochemical reactions the methoxylation of 1-formylpyrrolidine and the oxidation of 3-bromobenzyl alcohol were investigated to demonstrate that the optimal conditions can be located within the pre-defined multi-dimensional reaction parameter spaces without intervention of the operator by using the stable noisy optimization by branch and FIT (SNOBFIT) algorithm.
Collapse
Affiliation(s)
- Jie Ke
- School of Chemistry, 6123University of Nottingham, Nottingham, UK
| | - Chuang Gao
- School of Chemistry, 6123University of Nottingham, Nottingham, UK
- Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China, Ningbo, China
| | | | - Katherine E Jolley
- School of Chemistry, 6123University of Nottingham, Nottingham, UK
- School of Chemistry, University of Southampton, Southampton, UK
| | - Oscar de Frutos
- Centro de Investigación Lilly S.A., Alcobendas-Madrid, Spain
| | - Carlos Mateos
- Centro de Investigación Lilly S.A., Alcobendas-Madrid, Spain
| | - Juan A Rincón
- Centro de Investigación Lilly S.A., Alcobendas-Madrid, Spain
| | | | - Martyn Poliakoff
- School of Chemistry, 6123University of Nottingham, Nottingham, UK
| | - Michael W George
- School of Chemistry, 6123University of Nottingham, Nottingham, UK
- Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China, Ningbo, China
| |
Collapse
|
4
|
Crandall Z, Basemann K, Qi L, Windus TL. Rxn Rover: automation of chemical reactions with user-friendly, modular software. REACT CHEM ENG 2022. [DOI: 10.1039/d1re00265a] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Automation of chemical reactions through tools such as Rxn Rover in research and development is an enabling technology to reduce cost and waste management in technology transformations towards renewable feedstocks and energy in the chemical industry.
Collapse
Affiliation(s)
- Zachery Crandall
- U.S. DOE Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
| | - Kevin Basemann
- U.S. DOE Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
| | - Long Qi
- U.S. DOE Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Theresa L. Windus
- U.S. DOE Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
| |
Collapse
|
5
|
Jorayev P, Russo D, Tibbetts JD, Schweidtmann AM, Deutsch P, Bull SD, Lapkin AA. Multi-objective Bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.116938] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
|
6
|
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
| |
Collapse
|
7
|
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
| |
Collapse
|
8
|
Vasudevan N, Wimmer E, Barré E, Cortés‐Borda D, Rodriguez‐Zubiri M, Felpin F. Direct C−H Arylation of Indole‐3‐Acetic Acid Derivatives Enabled by an Autonomous Self‐Optimizing Flow Reactor. Adv Synth Catal 2020. [DOI: 10.1002/adsc.202001217] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- N. Vasudevan
- Université de Nantes CNRS CEISAM UMR 6230 2 rue de la Houssinière 44322 Nantes France
| | - Eric Wimmer
- Université de Nantes CNRS CEISAM UMR 6230 2 rue de la Houssinière 44322 Nantes France
| | - Elvina Barré
- Université de Nantes CNRS CEISAM UMR 6230 2 rue de la Houssinière 44322 Nantes France
| | - Daniel Cortés‐Borda
- Université de Nantes CNRS CEISAM UMR 6230 2 rue de la Houssinière 44322 Nantes France
| | | | | |
Collapse
|
9
|
Waldron C, Pankajakshan A, Quaglio M, Cao E, Galvanin F, Gavriilidis A. Closed-Loop Model-Based Design of Experiments for Kinetic Model Discrimination and Parameter Estimation: Benzoic Acid Esterification on a Heterogeneous Catalyst. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b04089] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Conor Waldron
- Department of Chemical Engineering, University College London, London WC1E 7JE, U.K
| | - Arun Pankajakshan
- Department of Chemical Engineering, University College London, London WC1E 7JE, U.K
| | - Marco Quaglio
- Department of Chemical Engineering, University College London, London WC1E 7JE, U.K
| | - Enhong Cao
- Department of Chemical Engineering, University College London, London WC1E 7JE, U.K
| | - Federico Galvanin
- Department of Chemical Engineering, University College London, London WC1E 7JE, U.K
| | - Asterios Gavriilidis
- Department of Chemical Engineering, University College London, London WC1E 7JE, U.K
| |
Collapse
|
10
|
Rubens M, Van Herck J, Junkers T. Automated Polymer Synthesis Platform for Integrated Conversion Targeting Based on Inline Benchtop NMR. ACS Macro Lett 2019; 8:1437-1441. [PMID: 35651185 DOI: 10.1021/acsmacrolett.9b00767] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
An automated polymer synthesis platform based on an inline low-field nuclear magnetic resonance spectrometer is developed. Flow chemistry and automated inline analyses are an excellent combination for automated kinetic screening and for self-optimizing reactions with programmable conversion targeting. By monitoring monomer conversion over a continuous range of reactor residence times, the platform is able to construct kinetic profiles of polymerizations in an accurate and efficient way. The machine-assisted self-optimization routine allows the reaction to be stopped at any given preselected conversion, giving rise to unprecedented reproducibility in polymer synthesis.
Collapse
Affiliation(s)
- Maarten Rubens
- Polymer Reaction Design Group, School of Chemistry, Monash University, 19 Rainforest Walk, Clayton, Victoria 3800, Australia
- Institute for Materials Research, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium
| | - Joren Van Herck
- Polymer Reaction Design Group, School of Chemistry, Monash University, 19 Rainforest Walk, Clayton, Victoria 3800, Australia
| | - Tanja Junkers
- Polymer Reaction Design Group, School of Chemistry, Monash University, 19 Rainforest Walk, Clayton, Victoria 3800, Australia
- Institute for Materials Research, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium
| |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
Elpa DP, Prabhu GRD, Wu SP, Tay KS, Urban PL. Automation of mass spectrometric detection of analytes and related workflows: A review. Talanta 2019; 208:120304. [PMID: 31816721 DOI: 10.1016/j.talanta.2019.120304] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 08/26/2019] [Accepted: 08/28/2019] [Indexed: 12/13/2022]
Abstract
The developments in mass spectrometry (MS) in the past few decades reveal the power and versatility of this technology. MS methods are utilized in routine analyses as well as research activities involving a broad range of analytes (elements and molecules) and countless matrices. However, manual MS analysis is gradually becoming a thing of the past. In this article, the available MS automation strategies are critically evaluated. Automation of analytical workflows culminating with MS detection encompasses involvement of automated operations in any of the steps related to sample handling/treatment before MS detection, sample introduction, MS data acquisition, and MS data processing. Automated MS workflows help to overcome the intrinsic limitations of MS methodology regarding reproducibility, throughput, and the expertise required to operate MS instruments. Such workflows often comprise automated off-line and on-line steps such as sampling, extraction, derivatization, and separation. The most common instrumental tools include autosamplers, multi-axis robots, flow injection systems, and lab-on-a-chip. Prototyping customized automated MS systems is a way to introduce non-standard automated features to MS workflows. The review highlights the enabling role of automated MS procedures in various sectors of academic research and industry. Examples include applications of automated MS workflows in bioscience, environmental studies, and exploration of the outer space.
Collapse
Affiliation(s)
- Decibel P Elpa
- Department of Applied Chemistry, National Chiao Tung University, 1001 University Rd., Hsinchu, 300, Taiwan; Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Rd., Hsinchu, 30013, Taiwan
| | - Gurpur Rakesh D Prabhu
- Department of Applied Chemistry, National Chiao Tung University, 1001 University Rd., Hsinchu, 300, Taiwan; Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Rd., Hsinchu, 30013, Taiwan
| | - Shu-Pao Wu
- Department of Applied Chemistry, National Chiao Tung University, 1001 University Rd., Hsinchu, 300, Taiwan.
| | - Kheng Soo Tay
- Department of Chemistry, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Pawel L Urban
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Rd., Hsinchu, 30013, Taiwan; Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, 101, Section 2, Kuang-Fu Rd., Hsinchu, 30013, Taiwan.
| |
Collapse
|
13
|
Amar Y, Schweidtmann AM, Deutsch P, Cao L, Lapkin A. Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis. Chem Sci 2019; 10:6697-6706. [PMID: 31367324 PMCID: PMC6625492 DOI: 10.1039/c9sc01844a] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 05/28/2019] [Indexed: 12/19/2022] Open
Abstract
Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)2(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral α-β unsaturated γ-lactam. With two simultaneous objectives - high conversion and high diastereomeric excess - the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories.
Collapse
Affiliation(s)
- Yehia Amar
- Department of Chemical Engineering and Biotechnology , University of Cambridge , Philippa Fawcett Drive , Cambridge , CB3 0AS , UK .
| | - Artur M Schweidtmann
- Aachener Verfahrenstechnik - Process Systems Engineering , RWTH Aachen University , Aachen , Germany
| | - Paul Deutsch
- UCB Pharma S.A. Allée de la Recherche , 60 1070 , Brussels , Belgium
| | - Liwei Cao
- Department of Chemical Engineering and Biotechnology , University of Cambridge , Philippa Fawcett Drive , Cambridge , CB3 0AS , UK .
- Cambridge Centre for Advanced Research and Education in Singapore Ltd. , 1 Create Way, CREATE Tower #05-05 , 138602 , Singapore
| | - Alexei Lapkin
- Department of Chemical Engineering and Biotechnology , University of Cambridge , Philippa Fawcett Drive , Cambridge , CB3 0AS , UK .
- Cambridge Centre for Advanced Research and Education in Singapore Ltd. , 1 Create Way, CREATE Tower #05-05 , 138602 , Singapore
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
Sivaramakrishnan K, Puliyanda A, Tefera DT, Ganesh A, Thirumalaivasan S, Prasad V. A Perspective on the Impact of Process Systems Engineering on Reaction Engineering. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b00280] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Kaushik Sivaramakrishnan
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Anjana Puliyanda
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Dereje Tamiru Tefera
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Ajay Ganesh
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Sushmitha Thirumalaivasan
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Vinay Prasad
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| |
Collapse
|
16
|
Rubens M, Vrijsen JH, Laun J, Junkers T. Precise Polymer Synthesis by Autonomous Self‐Optimizing Flow Reactors. Angew Chem Int Ed Engl 2019; 58:3183-3187. [DOI: 10.1002/anie.201810384] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 10/07/2018] [Indexed: 11/10/2022]
Affiliation(s)
- Maarten Rubens
- Hasselt University Martelarenlaan 42 3500 Hasselt Belgium
- Polymer Reaction Design GroupSchool of ChemistryMonash University 19 Rainforest Walk, Building 23 Clayton Vic 3800 Australia
| | | | - Joachim Laun
- Hasselt University Martelarenlaan 42 3500 Hasselt Belgium
| | - Tanja Junkers
- Hasselt University Martelarenlaan 42 3500 Hasselt Belgium
- Polymer Reaction Design GroupSchool of ChemistryMonash University 19 Rainforest Walk, Building 23 Clayton Vic 3800 Australia
| |
Collapse
|
17
|
|
18
|
Sagmeister P, Williams JD, Hone CA, Kappe CO. Laboratory of the future: a modular flow platform with multiple integrated PAT tools for multistep reactions. REACT CHEM ENG 2019. [DOI: 10.1039/c9re00087a] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The coupling of a modular microreactor platform, real-time inline analysis by IR and NMR, and online UPLC, leads to efficient optimization of a multistep organolithium transformation to a given product without the need for human intervention.
Collapse
Affiliation(s)
- Peter Sagmeister
- Center for Continuous Synthesis and Processing (CCFLOW)
- Research Center Pharmaceutical Engineering (RCPE)
- 8010 Graz
- Austria
- Institute of Chemistry
| | - Jason D. Williams
- Center for Continuous Synthesis and Processing (CCFLOW)
- Research Center Pharmaceutical Engineering (RCPE)
- 8010 Graz
- Austria
- Institute of Chemistry
| | - Christopher A. Hone
- Center for Continuous Synthesis and Processing (CCFLOW)
- Research Center Pharmaceutical Engineering (RCPE)
- 8010 Graz
- Austria
- Institute of Chemistry
| | - C. Oliver Kappe
- Center for Continuous Synthesis and Processing (CCFLOW)
- Research Center Pharmaceutical Engineering (RCPE)
- 8010 Graz
- Austria
- Institute of Chemistry
| |
Collapse
|
19
|
Wimmer E, Cortés-Borda D, Brochard S, Barré E, Truchet C, Felpin FX. An autonomous self-optimizing flow machine for the synthesis of pyridine–oxazoline (PyOX) ligands. REACT CHEM ENG 2019. [DOI: 10.1039/c9re00096h] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A continuous flow synthesis of PyOX ligands has been developed using an autonomous self-optimizing flow reactor.
Collapse
Affiliation(s)
- Eric Wimmer
- Université de Nantes
- CNRS UMR 6230
- CEISAM
- 44322 Nantes Cedex 3
- France
| | | | - Solène Brochard
- Université de Nantes
- CNRS UMR 6230
- CEISAM
- 44322 Nantes Cedex 3
- France
| | - Elvina Barré
- Université de Nantes
- CNRS UMR 6230
- CEISAM
- 44322 Nantes Cedex 3
- France
| | | | | |
Collapse
|
20
|
Fabry DC, Heddrich S, Sugiono E, Liauw MA, Rueping M. Adaptive and automated system-optimization for heterogeneous flow-hydrogenation reactions. REACT CHEM ENG 2019. [DOI: 10.1039/c9re00032a] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The autonomous hydrogenation of carbonyl compounds and N-heterocycles over solid catalysts with H2 is achieved in 3D parameter space by integrating analytics, control and hardware.
Collapse
Affiliation(s)
- David C. Fabry
- Institute of Organic Chemistry
- RWTH Aachen University
- Aachen D-52074
- Germany
| | - Steffen Heddrich
- Institute of Technical and Macromolecular Chemistry
- RWTH Aachen University
- 52074 Aachen
- Germany
| | - Erli Sugiono
- Institute of Organic Chemistry
- RWTH Aachen University
- Aachen D-52074
- Germany
| | - Marcel A. Liauw
- Institute of Technical and Macromolecular Chemistry
- RWTH Aachen University
- 52074 Aachen
- Germany
| | - Magnus Rueping
- Institute of Organic Chemistry
- RWTH Aachen University
- Aachen D-52074
- Germany
- KAUST Catalysis Center (KCC)
| |
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
Mateos C, Nieves-Remacha MJ, Rincón JA. Automated platforms for reaction self-optimization in flow. REACT CHEM ENG 2019. [DOI: 10.1039/c9re00116f] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
This article highlights recent progress in continuous flow self-optimizing platforms.
Collapse
Affiliation(s)
- Carlos Mateos
- Centro de Investigación Lilly S.A
- Alcobendas-Madrid 28108
- Spain
| | | | - Juan A. Rincón
- Centro de Investigación Lilly S.A
- Alcobendas-Madrid 28108
- Spain
| |
Collapse
|
23
|
Rubens M, Vrijsen JH, Laun J, Junkers T. Precise Polymer Synthesis by Autonomous Self‐Optimizing Flow Reactors. Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201810384] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Maarten Rubens
- Hasselt University Martelarenlaan 42 3500 Hasselt Belgium
- Polymer Reaction Design GroupSchool of ChemistryMonash University 19 Rainforest Walk, Building 23 Clayton Vic 3800 Australia
| | | | - Joachim Laun
- Hasselt University Martelarenlaan 42 3500 Hasselt Belgium
| | - Tanja Junkers
- Hasselt University Martelarenlaan 42 3500 Hasselt Belgium
- Polymer Reaction Design GroupSchool of ChemistryMonash University 19 Rainforest Walk, Building 23 Clayton Vic 3800 Australia
| |
Collapse
|
24
|
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
| |
Collapse
|