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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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Sadeghi S, Bateni F, Kim T, Son DY, Bennett JA, Orouji N, Punati VS, Stark C, Cerra TD, Awad R, Delgado-Licona F, Xu J, Mukhin N, Dickerson H, Reyes KG, Abolhasani M. Autonomous nanomanufacturing of lead-free metal halide perovskite nanocrystals using a self-driving fluidic lab. NANOSCALE 2024; 16:580-591. [PMID: 38116636 DOI: 10.1039/d3nr05034c] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Lead-based metal halide perovskite (MHP) nanocrystals (NCs) have emerged as a promising class of semiconducting nanomaterials for a wide range of optoelectronic and photoelectronic applications. However, the intrinsic lead toxicity of MHP NCs has significantly hampered their large-scale device applications. Copper-base MHP NCs with composition-tunable optical properties have emerged as a prominent lead-free MHP NC candidate. However, comprehensive synthesis space exploration, development, and synthesis science studies of copper-based MHP NCs have been limited by the manual nature of flask-based synthesis and characterization methods. In this study, we present an autonomous approach for the development of lead-free MHP NCs via seamless integration of a modular microfluidic platform with machine learning-assisted NC synthesis modeling and experiment selection to establish a self-driving fluidic lab for accelerated NC synthesis science studies. For the first time, a successful and reproducible in-flow synthesis of Cs3Cu2I5 NCs is presented. Autonomous experimentation is then employed for rapid in-flow synthesis science studies of Cs3Cu2I5 NCs. The autonomously generated experimental NC synthesis dataset is then utilized for fast-tracked synthetic route optimization of high-performing Cs3Cu2I5 NCs.
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Affiliation(s)
- Sina Sadeghi
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Fazel Bateni
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Taekhoon Kim
- Synthesis Technical Unit, Material Research Center, Samsung Advanced Institute of Technology, SEC, 130, Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Dae Yong Son
- Synthesis Technical Unit, Material Research Center, Samsung Advanced Institute of Technology, SEC, 130, Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Jeffrey A Bennett
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Negin Orouji
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Venkat S Punati
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Christine Stark
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Teagan D Cerra
- Department of Physics, Weber State University, Ogden, UT 84408, USA
| | - Rami Awad
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Fernando Delgado-Licona
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Jinge Xu
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Nikolai Mukhin
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Hannah Dickerson
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Kristofer G Reyes
- Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY 14260, USA
| | - Milad Abolhasani
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
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3
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Volk AA, Epps RW, Yonemoto DT, Masters BS, Castellano FN, Reyes KG, Abolhasani M. AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning. Nat Commun 2023; 14:1403. [PMID: 36918561 PMCID: PMC10015005 DOI: 10.1038/s41467-023-37139-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 03/02/2023] [Indexed: 03/16/2023] Open
Abstract
Closed-loop, autonomous experimentation enables accelerated and material-efficient exploration of large reaction spaces without the need for user intervention. However, autonomous exploration of advanced materials with complex, multi-step processes and data sparse environments remains a challenge. In this work, we present AlphaFlow, a self-driven fluidic lab capable of autonomous discovery of complex multi-step chemistries. AlphaFlow uses reinforcement learning integrated with a modular microdroplet reactor capable of performing reaction steps with variable sequence, phase separation, washing, and continuous in-situ spectral monitoring. To demonstrate the power of reinforcement learning toward high dimensionality multi-step chemistries, we use AlphaFlow to discover and optimize synthetic routes for shell-growth of core-shell semiconductor nanoparticles, inspired by colloidal atomic layer deposition (cALD). Without prior knowledge of conventional cALD parameters, AlphaFlow successfully identified and optimized a novel multi-step reaction route, with up to 40 parameters, that outperformed conventional sequences. Through this work, we demonstrate the capabilities of closed-loop, reinforcement learning-guided systems in exploring and solving challenges in multi-step nanoparticle syntheses, while relying solely on in-house generated data from a miniaturized microfluidic platform. Further application of AlphaFlow in multi-step chemistries beyond cALD can lead to accelerated fundamental knowledge generation as well as synthetic route discoveries and optimization.
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Affiliation(s)
- Amanda A Volk
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695-7905, USA
| | - Robert W Epps
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695-7905, USA
| | - Daniel T Yonemoto
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695-8204, USA
| | - Benjamin S Masters
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695-8204, USA
| | - Felix N Castellano
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695-8204, USA
| | - Kristofer G Reyes
- Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY, 14260, USA
| | - Milad Abolhasani
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695-7905, USA.
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5
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Abstract
Anisotropic heterostructures of colloidal nanocrystals embed size-, shape-, and composition-dependent electronic structure within variable three-dimensional morphology, enabling intricate design of solution-processable materials with high performance and programmable functionality. The key to designing and synthesizing such complex materials lies in understanding the fundamental thermodynamic and kinetic factors that govern nanocrystal growth. In this review, nanorod heterostructures, the simplest of anisotropic nanocrystal heterostructures, are discussed with respect to their growth mechanisms. The effects of crystal structure, surface faceting/energies, lattice strain, ligand sterics, precursor reactivity, and reaction temperature on the growth of nanorod heterostructures through heteroepitaxy and cation exchange reactions are explored with currently known examples. Understanding the role of various thermodynamic and kinetic parameters enables the controlled synthesis of complex nanorod heterostructures that can exhibit unique tailored properties. Selected application prospects arising from such capabilities are then discussed.
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Affiliation(s)
- Gryphon A Drake
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 United States
| | - Logan P Keating
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 United States
| | - Moonsub Shim
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 United States
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Volk AA, Campbell ZS, Ibrahim MYS, Bennett JA, Abolhasani M. Flow Chemistry: A Sustainable Voyage Through the Chemical Universe en Route to Smart Manufacturing. Annu Rev Chem Biomol Eng 2022; 13:45-72. [PMID: 35259931 DOI: 10.1146/annurev-chembioeng-092120-024449] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Microfluidic devices and systems have entered many areas of chemical engineering, and the rate of their adoption is only increasing. As we approach and adapt to the critical global challenges we face in the near future, it is important to consider the capabilities of flow chemistry and its applications in next-generation technologies for sustainability, energy production, and tailor-made specialty chemicals. We present the introduction of microfluidics into the fundamental unit operations of chemical engineering. We discuss the traits and advantages of microfluidic approaches to different reactive systems, both well-established and emerging, with a focus on the integration of modular microfluidic devices into high-efficiency experimental platforms for accelerated process optimization and intensified continuous manufacturing. Finally, we discuss the current state and new horizons in self-driven experimentation in flow chemistry for both intelligent exploration through the chemical universe and distributed manufacturing. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Amanda A Volk
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA; , , , ,
| | - Zachary S Campbell
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA; , , , ,
| | - Malek Y S Ibrahim
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA; , , , ,
| | - Jeffrey A Bennett
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA; , , , ,
| | - Milad Abolhasani
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA; , , , ,
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