1
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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; 124:9633-9732. [PMID: 39137296 PMCID: PMC11363023 DOI: 10.1021/acs.chemrev.4c00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [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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2
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Okada H, Maeda S. On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set. ACS OMEGA 2024; 9:7123-7131. [PMID: 38371820 PMCID: PMC10870292 DOI: 10.1021/acsomega.3c09066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/05/2024] [Accepted: 01/16/2024] [Indexed: 02/20/2024]
Abstract
Substrate optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools such as Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further by utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted substrate optimization, this study employs a computational Gibbs energy barrier data set in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as virtual experimental results and those with systematic and random noises as virtual computational results. The present numerical investigation shows that even the computational reactivity affected by noises of as much as 20 kJ/mol helps reduce the number of required experiments.
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Affiliation(s)
- Hiroaki Okada
- Graduate
School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, Hokkaido 060-8628, Japan
| | - Satoshi Maeda
- Department
of Chemistry, Graduate School of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Institute
for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido 001-0021, Japan
- ERATO
Maeda Artificial Intelligence for Chemical Reaction Design and Discovery
Project, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Research
and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0044, Japan
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3
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Haas C, Lübbesmeyer M, Jin EH, McDonald MA, Koscher BA, Guimond N, Di Rocco L, Kayser H, Leweke S, Niedenführ S, Nicholls R, Greeves E, Barber DM, Hillenbrand J, Volpin G, Jensen KF. Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening. ACS CENTRAL SCIENCE 2023; 9:307-317. [PMID: 36844498 PMCID: PMC9951288 DOI: 10.1021/acscentsci.2c01042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Indexed: 06/18/2023]
Abstract
Automation and digitalization solutions in the field of small molecule synthesis face new challenges for chemical reaction analysis, especially in the field of high-performance liquid chromatography (HPLC). Chromatographic data remains locked in vendors' hardware and software components, limiting their potential in automated workflows and data science applications. In this work, we present an open-source Python project called MOCCA for the analysis of HPLC-DAD (photodiode array detector) raw data. MOCCA provides a comprehensive set of data analysis features, including an automated peak deconvolution routine of known signals, even if overlapped with signals of unexpected impurities or side products. We highlight the broad applicability of MOCCA in four studies: (i) a simulation study to validate MOCCA's data analysis features; (ii) a reaction kinetics study on a Knoevenagel condensation reaction demonstrating MOCCA's peak deconvolution feature; (iii) a closed-loop optimization study for the alkylation of 2-pyridone without human control during data analysis; (iv) a well plate screening of categorical reaction parameters for a novel palladium-catalyzed cyanation of aryl halides employing O-protected cyanohydrins. By publishing MOCCA as a Python package with this work, we envision an open-source community project for chromatographic data analysis with the potential of further advancing its scope and capabilities.
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Affiliation(s)
- Christian
P. Haas
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Research
and Development, Small Molecules Technologies, Bayer AG, Crop Science Division, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Maximilian Lübbesmeyer
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Research
and Development, Small Molecules Technologies, Bayer AG, Crop Science Division, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Edward H. Jin
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Matthew A. McDonald
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Brent A. Koscher
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Nicolas Guimond
- Research
and Development, Small Molecules Technologies, Bayer AG, Crop Science Division, Alfred-Nobel-Straße 50, 40789 Monheim am Rhein, Germany
| | - Laura Di Rocco
- Chemical
& Pharmaceutical Development, Bayer
AG, Pharmaceuticals Division, Müllerstraße 178, 13353 Berlin, Germany
| | - Henning Kayser
- Research
and Development, Small Molecules Technologies, Bayer AG, Crop Science Division, Alfred-Nobel-Straße 50, 40789 Monheim am Rhein, Germany
| | - Samuel Leweke
- Applied
Mathematics, Bayer AG, Enabling Functions
Division, Kaiser-Wilhelm-Allee
1, 51368 Leverkusen, Germany
| | - Sebastian Niedenführ
- Research
and Development, Computational Life Science, Bayer AG, Crop Science Division, Alfred-Nobel-Straße 50, 40789 Monheim am Rhein, Germany
| | - Rachel Nicholls
- Research
and Development, Computational Life Science, Bayer AG, Crop Science Division, Alfred-Nobel-Straße 50, 40789 Monheim am Rhein, Germany
| | - Emily Greeves
- Research
and Development, Small Molecules Technologies, Bayer AG, Crop Science Division, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - David M. Barber
- Research
and Development, Weed Control Chemistry, Bayer AG, Crop Science Division, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Julius Hillenbrand
- Chemical
& Pharmaceutical Development, Bayer
AG, Pharmaceuticals Division, Friedrich-Ebert-Straße 475, 42117 Wuppertal, Germany
| | - Giulio Volpin
- Research
and Development, Small Molecules Technologies, Bayer AG, Crop Science Division, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Klavs F. Jensen
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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4
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Lin MH, Wolf JB, Sletten ET, Cambié D, Danglad-Flores J, Seeberger PH. Enabling Technologies in Carbohydrate Chemistry: Automated Glycan Assembly, Flow Chemistry and Data Science. Chembiochem 2023; 24:e202200607. [PMID: 36382494 DOI: 10.1002/cbic.202200607] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/15/2022] [Indexed: 11/17/2022]
Abstract
The synthesis of defined oligosaccharides is a complex task. Several enabling technologies have been introduced in the last two decades to facilitate synthetic access to these valuable biomolecules. In this concept, we describe the technological solutions that have advanced glycochemistry using automated glycan assembly, flow chemistry and data science as examples. We highlight how the synergies between these different technologies can further advance the field, with progress toward the realization of a self-driving lab for glycan synthesis.
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Affiliation(s)
- Mei-Huei Lin
- Department of Biomolecular Systems, Max-Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany.,Department of Chemistry and Biochemistry, Freie Universität Berlin, Arnimallee 22, 14195, Berlin, Germany
| | - Jakob B Wolf
- Department of Biomolecular Systems, Max-Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany.,Department of Chemistry and Biochemistry, Freie Universität Berlin, Arnimallee 22, 14195, Berlin, Germany
| | - Eric T Sletten
- Department of Biomolecular Systems, Max-Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - Dario Cambié
- Department of Biomolecular Systems, Max-Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - José Danglad-Flores
- Department of Biomolecular Systems, Max-Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - Peter H Seeberger
- Department of Biomolecular Systems, Max-Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany.,Department of Chemistry and Biochemistry, Freie Universität Berlin, Arnimallee 22, 14195, Berlin, Germany
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5
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Domingues NP, Moosavi SM, Talirz L, Jablonka KM, Ireland CP, Ebrahim FM, Smit B. Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF. Commun Chem 2022; 5:170. [PMID: 36697847 PMCID: PMC9814730 DOI: 10.1038/s42004-022-00785-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022] Open
Abstract
The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al2(OH)2TCPP) [H2TCPP = meso-tetra(4-carboxyphenyl)porphine], a promising material for carbon capture applications. Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). Here, we use a genetic algorithm to carry out a systematic search for the optimal synthesis conditions and a microwave-based high-throughput robotic platform for the syntheses. We show that, in just two generations, we could obtain excellent crystallinity and yield close to 80% in a much shorter reaction time (50 minutes). Moreover, by analyzing the failed and partially successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield.
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Affiliation(s)
- Nency P. Domingues
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland
| | - Seyed Mohamad Moosavi
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland ,grid.14095.390000 0000 9116 4836Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Leopold Talirz
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland ,grid.5333.60000000121839049Theory and Simulation of Materials (THEOS), School of Engineering (STI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Vaud Switzerland
| | - Kevin Maik Jablonka
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland
| | - Christopher P. Ireland
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland
| | - Fatmah Mish Ebrahim
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland ,grid.5335.00000000121885934Cavendish Laboratory, School of Physical Sciences, University of Cambridge, Cambridge, UK
| | - Berend Smit
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland
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6
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Wahl CB, Aykol M, Swisher JH, Montoya JH, Suram SK, Mirkin CA. Machine learning-accelerated design and synthesis of polyelemental heterostructures. SCIENCE ADVANCES 2021; 7:eabj5505. [PMID: 34936439 PMCID: PMC8694626 DOI: 10.1126/sciadv.abj5505] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 11/10/2021] [Indexed: 05/23/2023]
Abstract
In materials discovery efforts, synthetic capabilities far outpace the ability to extract meaningful data from them. To bridge this gap, machine learning methods are necessary to reduce the search space for identifying desired materials. Here, we present a machine learning–driven, closed-loop experimental process to guide the synthesis of polyelemental nanomaterials with targeted structural properties. By leveraging data from an eight-dimensional chemical space (Au-Ag-Cu-Co-Ni-Pd-Sn-Pt) as inputs, a Bayesian optimization algorithm is used to suggest previously unidentified nanoparticle compositions that target specific interfacial motifs for synthesis, results of which are iteratively shared back with the algorithm. This feedback loop resulted in successful syntheses of 18 heterojunction nanomaterials that are too complex to discover by chemical intuition alone, including extremely chemically complex biphasic nanoparticles reported to date. Platforms like the one developed here are poised to transform materials discovery across a wide swath of applications and industries.
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Affiliation(s)
- Carolin B. Wahl
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA
- International Institute for Nanotechnology, Northwestern University, Evanston, IL 60208, USA
| | | | - Jordan H. Swisher
- International Institute for Nanotechnology, Northwestern University, Evanston, IL 60208, USA
- Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
| | | | | | - Chad A. Mirkin
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA
- International Institute for Nanotechnology, Northwestern University, Evanston, IL 60208, USA
- Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
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