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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Strieth-Kalthoff F, Hao H, Rathore V, Derasp J, Gaudin T, Angello NH, Seifrid M, Trushina E, Guy M, Liu J, Tang X, Mamada M, Wang W, Tsagaantsooj T, Lavigne C, Pollice R, Wu TC, Hotta K, Bodo L, Li S, Haddadnia M, Wołos A, Roszak R, Ser CT, Bozal-Ginesta C, Hickman RJ, Vestfrid J, Aguilar-Granda A, Klimareva EL, Sigerson RC, Hou W, Gahler D, Lach S, Warzybok A, Borodin O, Rohrbach S, Sanchez-Lengeling B, Adachi C, Grzybowski BA, Cronin L, Hein JE, Burke MD, Aspuru-Guzik A. Delocalized, asynchronous, closed-loop discovery of organic laser emitters. Science 2024; 384:eadk9227. [PMID: 38753786 DOI: 10.1126/science.adk9227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 04/05/2024] [Indexed: 05/18/2024]
Abstract
Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enabled delocalized and asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based artificial intelligence experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing-and democratizing-scientific discovery.
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Affiliation(s)
- Felix Strieth-Kalthoff
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Han Hao
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Acceleration Consortium, University of Toronto, Toronto, ON, Canada
| | - Vandana Rathore
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Molecule Maker Lab, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Joshua Derasp
- Department of Chemistry, University of British Columbia, Vancouver, BC, Canada
| | - Théophile Gaudin
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Nicholas H Angello
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Molecule Maker Lab, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Molecule Maker Lab Institute, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Martin Seifrid
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Department of Materials Science and Engineering, North Carolina State University, Raleigh, NC, USA
| | | | - Mason Guy
- Department of Chemistry, University of British Columbia, Vancouver, BC, Canada
| | - Junliang Liu
- Department of Chemistry, University of British Columbia, Vancouver, BC, Canada
| | - Xun Tang
- Center for Organic Photonics and Electronics Research (OPERA), Kyushu University, Fukuoka, Japan
| | - Masashi Mamada
- Center for Organic Photonics and Electronics Research (OPERA), Kyushu University, Fukuoka, Japan
| | - Wesley Wang
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Molecule Maker Lab, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Molecule Maker Lab Institute, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Tuul Tsagaantsooj
- Center for Organic Photonics and Electronics Research (OPERA), Kyushu University, Fukuoka, Japan
| | - Cyrille Lavigne
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Robert Pollice
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Tony C Wu
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Kazuhiro Hotta
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Mitsubishi Chemical Corporation Science & Innovation Center, Kanagawa, Japan
| | - Leticia Bodo
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Shangyu Li
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Mohammad Haddadnia
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Agnieszka Wołos
- Allchemy Inc., Highland, IN, USA
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | - Rafał Roszak
- Allchemy Inc., Highland, IN, USA
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | - Cher Tian Ser
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Carlota Bozal-Ginesta
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Catalonia Institute for Energy Research, Barcelona, Spain
| | - Riley J Hickman
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Jenya Vestfrid
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Andrés Aguilar-Granda
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | | | | | - Wenduan Hou
- School of Chemistry, University of Glasgow, Glasgow, UK
| | - Daniel Gahler
- School of Chemistry, University of Glasgow, Glasgow, UK
| | - Slawomir Lach
- School of Chemistry, University of Glasgow, Glasgow, UK
| | - Adrian Warzybok
- School of Chemistry, University of Glasgow, Glasgow, UK
- Department of Chemical Physics, Jagiellonian University, Krakow, Poland
| | - Oleg Borodin
- School of Chemistry, University of Glasgow, Glasgow, UK
| | | | | | - Chihaya Adachi
- Center for Organic Photonics and Electronics Research (OPERA), Kyushu University, Fukuoka, Japan
| | - Bartosz A Grzybowski
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
- Center for Algorithmic and Robotized Synthesis, Institute for Basic Science, Ulsan, Republic of Korea
- Department of Chemistry, Ulsan Institute of Science and Technology, Ulsan, Republic of Korea
| | - Leroy Cronin
- Acceleration Consortium, University of Toronto, Toronto, ON, Canada
- School of Chemistry, University of Glasgow, Glasgow, UK
| | - Jason E Hein
- Acceleration Consortium, University of Toronto, Toronto, ON, Canada
- Department of Chemistry, University of British Columbia, Vancouver, BC, Canada
- Department of Chemistry, University of Bergen, Bergen, Norway
| | - Martin D Burke
- Acceleration Consortium, University of Toronto, Toronto, ON, Canada
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Molecule Maker Lab, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Molecule Maker Lab Institute, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Acceleration Consortium, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Materials Science and Engineering, University of Toronto, Toronto, ON, Canada
- Canadian Institute for Advanced Research (CIFAR), Toronto, ON, Canada
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3
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Yan C, Fyfe C, Minty L, Barrington H, Jamieson C, Reid M. Computer vision as a new paradigm for monitoring of solution and solid phase peptide synthesis. Chem Sci 2023; 14:11872-11880. [PMID: 37920332 PMCID: PMC10619640 DOI: 10.1039/d3sc01383a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 10/04/2023] [Indexed: 11/04/2023] Open
Abstract
We report a strategy for the camera-enabled non-contact colourimetric reaction monitoring and optimisation of amide bond formation, mediated by coupling reagents. For amide bond formation in solution phase, investigation of reactions mediated by HATU, PyAOP, and DIC/Oxyma evidenced correlations between colour parameters extracted from video data and conversion to amide product measured by off-line HPLC analysis of concentration. These correlations, supported by mutual information analysis, were further investigated using video recordings of solid phase peptide synthesis (SPPS), co-analysed by off-line HPLC to track remaining unreacted substrate in solution. An optimisation method of coupling time in SPPS was derived from ΔE (a measurement of colour contrast), giving comparable isolated peptide yield and purity at 65-95% reduced overall reaction time. The same colour data enabled data-rich monitoring of reaction rate attenuation, consisted with computationally-derived measures of amino acid steric bulk. These findings provide a foundation for exploring the use of camera technology and computer vision towards automated and online mechanistic profiling of SPPS.
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Affiliation(s)
- Chunhui Yan
- WestCHEM Department of Pure & Applied Chemistry, University of Strathclyde Glasgow UK
| | - Calum Fyfe
- WestCHEM Department of Pure & Applied Chemistry, University of Strathclyde Glasgow UK
| | - Laura Minty
- WestCHEM Department of Pure & Applied Chemistry, University of Strathclyde Glasgow UK
| | - Henry Barrington
- WestCHEM Department of Pure & Applied Chemistry, University of Strathclyde Glasgow UK
| | - Craig Jamieson
- WestCHEM Department of Pure & Applied Chemistry, University of Strathclyde Glasgow UK
| | - Marc Reid
- WestCHEM Department of Pure & Applied Chemistry, University of Strathclyde Glasgow UK
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4
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Salley D, Manzano JS, Kitson PJ, Cronin L. Robotic Modules for the Programmable Chemputation of Molecules and Materials. ACS CENTRAL SCIENCE 2023; 9:1525-1537. [PMID: 37637738 PMCID: PMC10450877 DOI: 10.1021/acscentsci.3c00304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Indexed: 08/29/2023]
Abstract
Before leveraging big data methods like machine learning and artificial intelligence (AI) in chemistry, there is an imperative need for an affordable, universal digitization standard. This mirrors the foundational requisites of the digital revolution, which demanded standard architectures with precise specifications. Recently, we have developed automated platforms tailored for chemical AI-driven exploration, including the synthesis of molecules, materials, nanomaterials, and formulations. Our focus has been on designing and constructing affordable standard hardware and software modules that serve as a blueprint for chemistry digitization across varied fields. Our platforms can be categorized into four types based on their applications: (i) discovery systems for the exploration of chemical space and novel reactivity, (ii) systems for the synthesis and manufacture of fine chemicals, (iii) platforms for formulation discovery and exploration, and (iv) systems for materials discovery and synthesis. We also highlight the convergent evolution of these platforms through shared hardware, firmware, and software alongside the creation of a unique programming language for chemical and material systems. This programming approach is essential for reliable synthesis, designing experiments, discovery, optimization, and establishing new collaboration standards. Furthermore, it is crucial for verifying literature findings, enhancing experimental outcome reliability, and fostering collaboration and sharing of unsuccessful experiments across different research labs.
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Affiliation(s)
- Daniel Salley
- School of Chemistry, University
of Glasgow, University Avenue, Glasgow G12 8QQ, U.K.
| | - J. Sebastián Manzano
- School of Chemistry, University
of Glasgow, University Avenue, Glasgow G12 8QQ, U.K.
| | - Philip J. Kitson
- School of Chemistry, University
of Glasgow, University Avenue, Glasgow G12 8QQ, U.K.
| | - Leroy Cronin
- School of Chemistry, University
of Glasgow, University Avenue, Glasgow G12 8QQ, U.K.
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5
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Yan C, Cowie M, Howcutt C, Wheelhouse KMP, Hodnett NS, Kollie M, Gildea M, Goodfellow MH, Reid M. Computer vision for non-contact monitoring of catalyst degradation and product formation kinetics. Chem Sci 2023; 14:5323-5331. [PMID: 37234891 PMCID: PMC10208035 DOI: 10.1039/d2sc05702f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/27/2023] [Indexed: 08/24/2023] Open
Abstract
We report a computer vision strategy for the extraction and colorimetric analysis of catalyst degradation and product-formation kinetics from video footage. The degradation of palladium(ii) pre-catalyst systems to form 'Pd black' is investigated as a widely relevant case study for catalysis and materials chemistries. Beyond the study of catalysts in isolation, investigation of Pd-catalyzed Miyaura borylation reactions revealed informative correlations between colour parameters (most notably ΔE, a colour-agnostic measure of contrast change) and the concentration of product measured by off-line analysis (NMR and LC-MS). The breakdown of such correlations helped inform conditions under which reaction vessels were compromised by air ingress. These findings present opportunities to expand the toolbox of non-invasive analytical techniques, operationally cheaper and simpler to implement than common spectroscopic methods. The approach introduces the capability of analyzing the macroscopic 'bulk' for the study of reaction kinetics in complex mixtures, in complement to the more common study of microscopic and molecular specifics.
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Affiliation(s)
- Chunhui Yan
- WestCHEM Department of Pure & Applied Chemistry University of Strathclyde Glasgow UK
| | - Megan Cowie
- WestCHEM Department of Pure & Applied Chemistry University of Strathclyde Glasgow UK
| | - Calum Howcutt
- WestCHEM Department of Pure & Applied Chemistry University of Strathclyde Glasgow UK
| | | | | | - Martin Kollie
- WestCHEM Department of Pure & Applied Chemistry University of Strathclyde Glasgow UK
| | - Martin Gildea
- WestCHEM Department of Pure & Applied Chemistry University of Strathclyde Glasgow UK
| | - Martin H Goodfellow
- WestCHEM Department of Pure & Applied Chemistry University of Strathclyde Glasgow UK
| | - Marc Reid
- WestCHEM Department of Pure & Applied Chemistry University of Strathclyde Glasgow UK
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6
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Abstract
Skin metabolites show huge potential for use in clinical diagnostics. However, skin sampling and analysis workflows are tedious and time-consuming. Here, we demonstrate a vending-machine-style skin excretion sensing platform based on hydrogel-assisted sampling of skin metabolites. In this sensing platform, a sampling probe with hydrogel is held by a robotic arm. The robotic arm manoeuvres the probe to press it onto the forearm of a human subject. Due to the highly hydrophilic nature of the hydrogel, water-soluble metabolites─released by skin─are collected into the hydrogel, leaving behind the nonpolar metabolites. The probe is then inserted into a custom-made open port sampling interface coupled to an electrospray ion source of a high-resolution quadrupole-time-of-flight mass spectrometer. Metabolites in the hydrogel are immediately extracted by a solvent liquid junction in the interface and analyzed using the mass spectrometer. The ion current of the target analyte is displayed on a customized graphical user interface, which can also be used to control the key components of the analytical platform. The automated sampling and analysis workflow starts after the user inserts coins or presents an insurance card, presses a button, and extends an arm on the sampling area. The platform relies on low-cost mechanical and electronic modules (a robotic arm, a single-board computer, and two microcontroller boards). The limits of detection for standard analytes─arginine, citrulline, and histidine─embedded in agarose gel beds were 148, 205, and 199 nM, respectively. Various low-molecular-weight metabolites from human skin have been identified with the high-resolution mass spectrometer.
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Affiliation(s)
- Kai-Chiang Yu
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu300044, Taiwan
| | - Chun-Yao Hsu
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu300044, Taiwan
| | - Gurpur Rakesh D Prabhu
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu300044, Taiwan
| | - Hsien-Yi Chiu
- Department of Medical Research, National Taiwan University Hospital Hsin-Chu Branch, 25 Jingguo Road, Hsinchu300, Taiwan.,Department of Dermatology, National Taiwan University Hospital Hsin-Chu Branch, 25 Jingguo Road, Hsinchu300, Taiwan.,Department of Dermatology, National Taiwan University Hospital, 7 Chung Shan S. Road, Taipei100, Taiwan.,Department of Dermatology, College of Medicine, National Taiwan University, 1 Jen Ai Road, Taipei100, Taiwan
| | - Pawel L Urban
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu300044, Taiwan.,Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu300044, Taiwan
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7
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Sarkar C, Das B, Rawat VS, Wahlang JB, Nongpiur A, Tiewsoh I, Lyngdoh NM, Das D, Bidarolli M, Sony HT. Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development. Int J Mol Sci 2023; 24:ijms24032026. [PMID: 36768346 PMCID: PMC9916967 DOI: 10.3390/ijms24032026] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 01/22/2023] Open
Abstract
The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.
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Affiliation(s)
- Chayna Sarkar
- Department of Pharmacology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Biswadeep Das
- Department of Pharmacology, All India Institute of Medical Sciences (AIIMS), Virbhadra Road, Rishikesh 249203, Uttarakhand, India
- Correspondence: ; Tel./Fax: +91-135-708-856-0009
| | - Vikram Singh Rawat
- Department of Psychiatry, All India Institute of Medical Sciences (AIIMS), Virbhadra Road, Rishikesh 249203, Uttarakhand, India
| | - Julie Birdie Wahlang
- Department of Pharmacology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Arvind Nongpiur
- Department of Psychiatry, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Iadarilang Tiewsoh
- Department of Medicine, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Nari M. Lyngdoh
- Department of Anesthesiology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Debasmita Das
- Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Road, Katpadi, Vellore 632014, Tamil Nadu, India
| | - Manjunath Bidarolli
- Department of Pharmacology, All India Institute of Medical Sciences (AIIMS), Virbhadra Road, Rishikesh 249203, Uttarakhand, India
| | - Hannah Theresa Sony
- Department of Pharmacology, All India Institute of Medical Sciences (AIIMS), Virbhadra Road, Rishikesh 249203, Uttarakhand, India
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8
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Keesey R, LeSuer R, Schrier J. Sidekick: A Low-Cost Open-Source 3D-printed liquid dispensing robot. HARDWAREX 2022; 12:e00319. [PMID: 35677813 PMCID: PMC9168727 DOI: 10.1016/j.ohx.2022.e00319] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/23/2022] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
The Sidekick is a desktop liquid dispenser, compatible with standard SBS microplates and designed for accessible laboratory automation. It features an armature-based motion system and a fully 3D-printed chassis to reduce overall mechanical complexity and accommodate user modification. Liquid dispensing is achieved with four commercially available solenoid driven positive displacement pumps that deliver liquid in 10 µL increments. A Raspberry Pi Pico RP2040 processor programmed in MicroPython is used for control, and exposes a USB serial interface for users to submit commands using either a simple vocabulary of commands or a subset of G-Code. At a total cost of $710 USD, the Sidekick offers laboratories an easy to build, easily maintained, open-source liquid dispensing system for both research and pedagogical introductions to lab automation.
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Affiliation(s)
- Rodolfo Keesey
- Department of Chemistry, Fordham University, 441 E. Fordham Road, The Bronx, NY 10458, USA
| | - Robert LeSuer
- Department of Chemistry and Biochemistry, SUNY Brockport, 350 New Campus Drive, Brockport, NY 14420, USA
| | - Joshua Schrier
- Department of Chemistry, Fordham University, 441 E. Fordham Road, The Bronx, NY 10458, USA
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9
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Harris Y, Sason H, Niezni D, Shamay Y. Automated discovery of nanomaterials via drug aggregation induced emission. Biomaterials 2022; 289:121800. [PMID: 36166893 DOI: 10.1016/j.biomaterials.2022.121800] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 08/30/2022] [Accepted: 09/07/2022] [Indexed: 12/19/2022]
Abstract
Nanoformulations of small molecule drugs are essential to effectively deliver them and treat a wide range of diseases. They are normally complex to develop, lack predictability, and exhibit low drug loading. Recently, nanoparticles made via co-assembly of hydrophobic drugs and organic dyes, exhibited drug-loading of up to 90% with high predictability from the drug structure. However, these particles have relatively short stability and can formulate only a small fraction of the drug space. Here, we developed an automated workflow to synthesize and select novel dye stabilizers, based on their ability to inhibit drug aggregation-induced emission (AIE). We first screened and identified 10 drugs with previously unknown strong AIE activity and exploited this trait to automatically synthesize and select a new ultra-stabilizer named R595. Interestingly, it shares several synthetic similarities and advantages with polydopamine. We found that R595 is superior to myriad types of excipients and solubilizers such as cyclodextrins, poloxamers, albumin, and previously published organic dyes, in both long-term stability and drug compatibility. We investigated the biodistribution, pharmacokinetics, safety and efficacy of the AIEgenic MEK inhibitor trametinib-R595 nanoparticles in vitro and in vivo and demonstrated that they are non-toxic and effective in KRAS driven colon and lung cancer models.
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Affiliation(s)
- Yuval Harris
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Hagit Sason
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Danna Niezni
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Yosi Shamay
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
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10
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Bai J, Cao L, Mosbach S, Akroyd J, Lapkin AA, Kraft M. From Platform to Knowledge Graph: Evolution of Laboratory Automation. JACS AU 2022; 2:292-309. [PMID: 35252980 PMCID: PMC8889618 DOI: 10.1021/jacsau.1c00438] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Indexed: 05/19/2023]
Abstract
High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.
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Affiliation(s)
- Jiaru Bai
- 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
| | - Sebastian Mosbach
- 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), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
| | - Jethro Akroyd
- 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), CREATE Tower #05-05, 1 Create Way, 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), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
| | - Markus Kraft
- 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), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
- School
of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459 Singapore
- The
Alan Turing Institute, London NW1 2DB, United Kingdom
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11
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Sagandira CR, Nqeketo S, Mhlana K, Sonti T, Gaqa S, Watts P. Towards 4th industrial revolution efficient and sustainable continuous flow manufacturing of active pharmaceutical ingredients. REACT CHEM ENG 2022. [DOI: 10.1039/d1re00483b] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The convergence of end-to-end continuous flow synthesis with downstream processing, process analytical technology (PAT), artificial intelligence (AI), machine learning and automation in ensuring improved accessibility of quality medicines on demand.
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Affiliation(s)
| | - Sinazo Nqeketo
- Nelson Mandela University, University Way, Port Elizabeth, 6031, South Africa
| | - Kanyisile Mhlana
- Nelson Mandela University, University Way, Port Elizabeth, 6031, South Africa
| | - Thembela Sonti
- Nelson Mandela University, University Way, Port Elizabeth, 6031, South Africa
| | - Sibongiseni Gaqa
- Nelson Mandela University, University Way, Port Elizabeth, 6031, South Africa
| | - Paul Watts
- Nelson Mandela University, University Way, Port Elizabeth, 6031, South Africa
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12
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Chang CW, Lin MH, Chan CK, Su KY, Wu CH, Lo WC, Lam S, Cheng YT, Liao PH, Wong CH, Wang CC. Automated Quantification of Hydroxyl Reactivities: Prediction of Glycosylation Reactions. Angew Chem Int Ed Engl 2021; 60:12413-12423. [PMID: 33634934 DOI: 10.1002/anie.202013909] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 02/07/2021] [Indexed: 12/17/2022]
Abstract
The stereoselectivity and yield in glycosylation reactions are paramount but unpredictable. We have developed a database of acceptor nucleophilic constants (Aka) to quantify the nucleophilicity of hydroxyl groups in glycosylation influenced by the steric, electronic and structural effects, providing a connection between experiments and computer algorithms. The subtle reactivity differences among the hydroxyl groups on various carbohydrate molecules can be defined by Aka, which is easily accessible by a simple and convenient automation system to assure high reproducibility and accuracy. A diverse range of glycosylation donors and acceptors with well-defined reactivity and promoters were organized and processed by the designed software program "GlycoComputer" for prediction of glycosylation reactions without involving sophisticated computational processing. The importance of Aka was further verified by random forest algorithm, and the applicability was tested by the synthesis of a Lewis A skeleton to show that the stereoselectivity and yield can be accurately estimated.
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Affiliation(s)
- Chun-Wei Chang
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan
| | - Mei-Huei Lin
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan
| | - Chieh-Kai Chan
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan
| | - Kuan-Yu Su
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan
| | - Chia-Hui Wu
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan
| | - Wei-Chih Lo
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan
| | - Sarah Lam
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan
| | - Yu-Ting Cheng
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan
| | - Pin-Hsuan Liao
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan
| | - Chi-Huey Wong
- The Genomics Research Center, Academia Sinica, Taipei, 115, Taiwan.,Department of Chemistry, The Scripps Research Institute, 10550 N Torrey Pines Road, La Jolla, 92037, USA
| | - Cheng-Chung Wang
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan.,Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program (TIGP), Academia Sinica, Taipei, 115, Taiwan
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13
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Chang C, Lin M, Chan C, Su K, Wu C, Lo W, Lam S, Cheng Y, Liao P, Wong C, Wang C. Automated Quantification of Hydroxyl Reactivities: Prediction of Glycosylation Reactions. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202013909] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Chun‐Wei Chang
- Institute of Chemistry Academia Sinica Taipei 115 Taiwan
| | - Mei‐Huei Lin
- Institute of Chemistry Academia Sinica Taipei 115 Taiwan
| | - Chieh‐Kai Chan
- Institute of Chemistry Academia Sinica Taipei 115 Taiwan
| | - Kuan‐Yu Su
- Institute of Chemistry Academia Sinica Taipei 115 Taiwan
| | - Chia‐Hui Wu
- Institute of Chemistry Academia Sinica Taipei 115 Taiwan
| | - Wei‐Chih Lo
- Institute of Chemistry Academia Sinica Taipei 115 Taiwan
| | - Sarah Lam
- Institute of Chemistry Academia Sinica Taipei 115 Taiwan
| | - Yu‐Ting Cheng
- Institute of Chemistry Academia Sinica Taipei 115 Taiwan
| | - Pin‐Hsuan Liao
- Institute of Chemistry Academia Sinica Taipei 115 Taiwan
| | - Chi‐Huey Wong
- The Genomics Research Center Academia Sinica Taipei 115 Taiwan
- Department of Chemistry The Scripps Research Institute 10550 N Torrey Pines Road La Jolla 92037 USA
| | - Cheng‐Chung Wang
- Institute of Chemistry Academia Sinica Taipei 115 Taiwan
- Chemical Biology and Molecular Biophysics Program Taiwan International Graduate Program (TIGP) Academia Sinica Taipei 115 Taiwan
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14
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Mo F, Qiu D, Zhang L, Wang J. Recent Development of Aryl Diazonium Chemistry for the Derivatization of Aromatic Compounds. Chem Rev 2021; 121:5741-5829. [DOI: 10.1021/acs.chemrev.0c01030] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Fanyang Mo
- Department of Energy and Resources Engineering, College of Engineering, Peking University, Beijing 100871, China
- School of Materials Science and Engineering, Peking University, Beijing 100871, China
| | - Di Qiu
- Tianjin Key Laboratory of Structure and Performance for Functional Molecules, College of Chemistry, Tianjin Normal University, Tianjin 300387, China
| | - Lei Zhang
- Department of Energy and Resources Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Jianbo Wang
- Beijing National Laboratory of Molecular Sciences (BNLMS), Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry, Peking University, Beijing 100871, China
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15
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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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16
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Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions. NANOMATERIALS 2021; 11:nano11030619. [PMID: 33801472 PMCID: PMC8000792 DOI: 10.3390/nano11030619] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/25/2021] [Accepted: 02/25/2021] [Indexed: 12/17/2022]
Abstract
Innovations often play an essential role in the acceleration of the new functional materials discovery. The success and applicability of the synthesis results with new chemical compounds and materials largely depend on the previous experience of the researcher himself and the modernity of the equipment used in the laboratory. Artificial intelligence (AI) technologies are the next step in developing the solution for practical problems in science, including the development of new materials. Those technologies go broadly beyond the borders of a computer science branch and give new insights and practical possibilities within the far areas of expertise and chemistry applications. One of the attractive challenges is an automated new functional material synthesis driven by AI. However, while having many years of hands-on experience, chemistry specialists have a vague picture of AI. To strengthen and underline AI's role in materials discovery, a short introduction is given to the essential technologies, and the machine learning process is explained. After this review, this review summarizes the recent studies of new strategies that help automate and accelerate the development of new functional materials. Moreover, automatized laboratories' self-driving cycle could benefit from using AI algorithms to optimize new functional nanomaterials' synthetic routes. Despite the fact that such technologies will shape material science in the nearest future, we note the intelligent use of algorithms and automation is required for novel discoveries.
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17
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Daley SK, Cordell GA. Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix. Nat Prod Commun 2021. [DOI: 10.1177/1934578x211003029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.
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Affiliation(s)
| | - Geoffrey A. Cordell
- Natural Products Inc., Evanston, IL, USA
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, USA
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18
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Sivo A, Galaverna RDS, Gomes GR, Pastre JC, Vilé G. From circular synthesis to material manufacturing: advances, challenges, and future steps for using flow chemistry in novel application area. REACT CHEM ENG 2021. [DOI: 10.1039/d0re00411a] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
We review the emerging use of flow technologies for circular chemistry and material manufacturing, highlighting advances, challenges, and future directions.
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Affiliation(s)
- Alessandra Sivo
- Department of Chemistry
- Materials and Chemical Engineering “Giulio Natta”
- Politecnico di Milano
- IT-20131 Milano
- Italy
| | | | | | | | - Gianvito Vilé
- Department of Chemistry
- Materials and Chemical Engineering “Giulio Natta”
- Politecnico di Milano
- IT-20131 Milano
- Italy
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19
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Abstract
Periodical transformation of ferroin to ferriin is accompanied by changes in magnetic properties of liquids during Belousov–Zhabotinsky (BZ) reaction malonic acid, sodium bromide, sodium bromate, ferroin, and sulfuric acid. Instead of the earlier studied oscillation of microwave conductivity accompanying an oscillating reaction, we propose a flash technique to interrupt the BZ reaction by rapid freezing. Rapid cooling of a solution during chemical oscillations results in a frozen system with a fixed concentration of paramagnetic centers Fe3+. EPR spectrum recorded at different stages of the interrupted reaction corresponds to the exact concentration of the ferroin and ferriin components. Following unfreezing unblocks the BZ reaction, and oscillations are still observed. A simulated spectrum allows one to distinguish two groups of Fe3+ ions of different symmetries. The obtained results are important to explain the earlier observed effect of inhomogeneous magnetic field on BZ reaction front velocity.
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20
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Chang CW, Lin MH, Wang CC. Statistical Analysis of Glycosylation Reactions. Chemistry 2020; 27:2556-2568. [PMID: 32939892 DOI: 10.1002/chem.202003105] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/15/2020] [Indexed: 12/27/2022]
Abstract
Chemical synthesis is one of the practical approaches to access carbohydrate-based natural products and their derivatives with high quality and in a large quantity. However, stereoselectivity during the glycosylation reaction is the main challenge because the reaction can generate both α- and β-glycosides. The main focus of the present article is the concept of recent mechanistic studies that have applied statistical analysis and quantitation for defining stereoselective changes during the reaction process. Based on experimental evidence, a detailed discussion associated with the mechanism and degree of influence affecting the stereoselective outcome of glycosylation is included.
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Affiliation(s)
- Chun-Wei Chang
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan
| | - Mei-Huei Lin
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan
| | - Cheng-Chung Wang
- Institute of Chemistry, Academia Sinica, Taipei, 115, Taiwan.,Chemical Biology and Molecular Biophysics Program (Taiwan), International Graduate Program (TIGP), Academia Sinica, Taipei, 115, Taiwan
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21
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Park NH, Zubarev DY, Hedrick JL, Kiyek V, Corbet C, Lottier S. A Recommender System for Inverse Design of Polycarbonates and Polyesters. Macromolecules 2020. [DOI: 10.1021/acs.macromol.0c02127] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Affiliation(s)
- Nathaniel H. Park
- IBM Research−Almaden, 650 Harry Rd., San Jose, California 95120, United States
| | - Dmitry Yu. Zubarev
- IBM Research−Almaden, 650 Harry Rd., San Jose, California 95120, United States
| | - James L. Hedrick
- IBM Research−Almaden, 650 Harry Rd., San Jose, California 95120, United States
| | - Vivien Kiyek
- IBM Research−Almaden, 650 Harry Rd., San Jose, California 95120, United States
| | - Christiaan Corbet
- IBM Research−Almaden, 650 Harry Rd., San Jose, California 95120, United States
| | - Simon Lottier
- IBM Research−Almaden, 650 Harry Rd., San Jose, California 95120, United States
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22
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Mehr SHM, Craven M, Leonov AI, Keenan G, Cronin L. A universal system for digitization and automatic execution of the chemical synthesis literature. Science 2020; 370:101-108. [PMID: 33004517 DOI: 10.1126/science.abc2986] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/16/2020] [Accepted: 07/27/2020] [Indexed: 12/13/2022]
Abstract
Robotic systems for chemical synthesis are growing in popularity but can be difficult to run and maintain because of the lack of a standard operating system or capacity for direct access to the literature through natural language processing. Here we show an extendable chemical execution architecture that can be populated by automatically reading the literature, leading to a universal autonomous workflow. The robotic synthesis code can be corrected in natural language without any programming knowledge and, because of the standard, is hardware independent. This chemical code can then be combined with a graph describing the hardware modules and compiled into platform-specific, low-level robotic instructions for execution. We showcase automated syntheses of 12 compounds from the literature, including the analgesic lidocaine, the Dess-Martin periodinane oxidation reagent, and the fluorinating agent AlkylFluor.
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Affiliation(s)
- S Hessam M Mehr
- School of Chemistry, University of Glasgow, Glasgow G12 8QQ, UK
| | - Matthew Craven
- School of Chemistry, University of Glasgow, Glasgow G12 8QQ, UK
| | - Artem I Leonov
- School of Chemistry, University of Glasgow, Glasgow G12 8QQ, UK
| | - Graham Keenan
- School of Chemistry, University of Glasgow, Glasgow G12 8QQ, UK
| | - Leroy Cronin
- School of Chemistry, University of Glasgow, Glasgow G12 8QQ, UK.
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23
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Zaquen N, Rubens M, Corrigan N, Xu J, Zetterlund PB, Boyer C, Junkers T. Polymer Synthesis in Continuous Flow Reactors. Prog Polym Sci 2020. [DOI: 10.1016/j.progpolymsci.2020.101256] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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24
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Abstract
With the rapid development of high technology, chemical science is not as it used to be a century ago. Many chemists acquire and utilize skills that are well beyond the traditional definition of chemistry. The digital age has transformed chemistry laboratories. One aspect of this transformation is the progressing implementation of electronics and computer science in chemistry research. In the past decade, numerous chemistry-oriented studies have benefited from the implementation of electronic modules, including microcontroller boards (MCBs), single-board computers (SBCs), professional grade control and data acquisition systems, as well as field-programmable gate arrays (FPGAs). In particular, MCBs and SBCs provide good value for money. The application areas for electronic modules in chemistry research include construction of simple detection systems based on spectrophotometry and spectrofluorometry principles, customizing laboratory devices for automation of common laboratory practices, control of reaction systems (batch- and flow-based), extraction systems, chromatographic and electrophoretic systems, microfluidic systems (classical and nonclassical), custom-built polymerase chain reaction devices, gas-phase analyte detection systems, chemical robots and drones, construction of FPGA-based imaging systems, and the Internet-of-Chemical-Things. The technology is easy to handle, and many chemists have managed to train themselves in its implementation. The only major obstacle in its implementation is probably one's imagination.
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Affiliation(s)
- Gurpur Rakesh D Prabhu
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan.,Department of Applied Chemistry, National Chiao Tung University, 1001 University Road, Hsinchu, 300, Taiwan
| | - Pawel L Urban
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan.,Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan
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25
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Damer B, Deamer D. The Hot Spring Hypothesis for an Origin of Life. ASTROBIOLOGY 2020; 20:429-452. [PMID: 31841362 PMCID: PMC7133448 DOI: 10.1089/ast.2019.2045] [Citation(s) in RCA: 164] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 10/23/2019] [Indexed: 05/05/2023]
Abstract
We present a testable hypothesis related to an origin of life on land in which fluctuating volcanic hot spring pools play a central role. The hypothesis is based on experimental evidence that lipid-encapsulated polymers can be synthesized by cycles of hydration and dehydration to form protocells. Drawing on metaphors from the bootstrapping of a simple computer operating system, we show how protocells cycling through wet, dry, and moist phases will subject polymers to combinatorial selection and draw structural and catalytic functions out of initially random sequences, including structural stabilization, pore formation, and primitive metabolic activity. We propose that protocells aggregating into a hydrogel in the intermediate moist phase of wet-dry cycles represent a primitive progenote system. Progenote populations can undergo selection and distribution, construct niches in new environments, and enable a sharing network effect that can collectively evolve them into the first microbial communities. Laboratory and field experiments testing the first steps of the scenario are summarized. The scenario is then placed in a geological setting on the early Earth to suggest a plausible pathway from life's origin in chemically optimal freshwater hot spring pools to the emergence of microbial communities tolerant to more extreme conditions in dilute lakes and salty conditions in marine environments. A continuity is observed for biogenesis beginning with simple protocell aggregates, through the transitional form of the progenote, to robust microbial mats that leave the fossil imprints of stromatolites so representative in the rock record. A roadmap to future testing of the hypothesis is presented. We compare the oceanic vent with land-based pool scenarios for an origin of life and explore their implications for subsequent evolution to multicellular life such as plants. We conclude by utilizing the hypothesis to posit where life might also have emerged in habitats such as Mars or Saturn's icy moon Enceladus. "To postulate one fortuitously catalyzed reaction, perhaps catalyzed by a metal ion, might be reasonable, but to postulate a suite of them is to appeal to magic." -Leslie Orgel.
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Affiliation(s)
- Bruce Damer
- Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - David Deamer
- Department of Biomolecular Engineering, University of California, Santa Cruz, California
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26
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Facilitating chemical and biochemical experiments with electronic microcontrollers and single-board computers. Nat Protoc 2020; 15:925-990. [PMID: 31996842 DOI: 10.1038/s41596-019-0272-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Accepted: 11/18/2019] [Indexed: 11/08/2022]
Abstract
Since the advent of modern science, researchers have had to rely on their technical skills or the support of specialized workshops to construct analytical instruments. The notion of the 'fourth industrial revolution' promotes construction of customized systems by individuals using widely available, inexpensive electronic modules. This protocol shows how chemists and biochemists can utilize a broad range of microcontroller boards (MCBs) and single-board computers (SBCs) to improve experimental designs and address scientific questions. We provide seven example procedures for laboratory routines that can be expedited by implementing this technology: (i) injection of microliter-volume liquid plugs into microscale capillaries for low-volume assays; (ii) transfer of liquid extract to a mass spectrometer; (iii) liquid-gas extraction of volatile organic compounds (called 'fizzy extraction'), followed by mass spectrometric detection; (iv) monitoring of experimental conditions over the Internet cloud in real time; (v) transfer of analytes to a mass spectrometer via a liquid microjunction interface, data acquisition, and data deposition into the Internet cloud; (vi) feedback control of a biochemical reaction; and (vii) optimization of sample flow rate in direct-infusion mass spectrometry. The protocol constitutes a primer for chemists and biochemists who would like to take advantage of MCBs and SBCs in daily experimentation. It is assumed that the readers have not attended any courses related to electronics or programming. Using the instructions provided in this protocol and the cited material, readers should be able to assemble simple systems to facilitate various procedures performed in chemical and biochemical laboratories in 1-2 d.
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Sharma MK, Raval J, Ahn GN, Kim DP, Kulkarni AA. Assessing the impact of deviations in optimized multistep flow synthesis on the scale-up. REACT CHEM ENG 2020. [DOI: 10.1039/d0re00025f] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
This manuscript highlights the unavoidable connection between manual and self-optimized flow synthesis protocols for multistep flow synthesis and its scale-up.
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Affiliation(s)
- M. K. Sharma
- Chemical Engineering and Process Development Division
- CSIR-National Chemical Laboratory
- Pune – 411008
- India
- AcSIR
| | - J. Raval
- Chemical Engineering and Process Development Division
- CSIR-National Chemical Laboratory
- Pune – 411008
- India
- AcSIR
| | - Gwang-Noh Ahn
- Center for Intelligent Microprocess of Pharmaceutical Synthesis
- Department of Chemical Engineering
- Pohang University of Science and Technology (POSTECH)
- Pohang 37673
- Korea
| | - Dong-Pyo Kim
- Center for Intelligent Microprocess of Pharmaceutical Synthesis
- Department of Chemical Engineering
- Pohang University of Science and Technology (POSTECH)
- Pohang 37673
- Korea
| | - A. A. Kulkarni
- Chemical Engineering and Process Development Division
- CSIR-National Chemical Laboratory
- Pune – 411008
- India
- AcSIR
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Toyao T, Maeno Z, Takakusagi S, Kamachi T, Takigawa I, Shimizu KI. Machine Learning for Catalysis Informatics: Recent Applications and Prospects. ACS Catal 2019. [DOI: 10.1021/acscatal.9b04186] [Citation(s) in RCA: 189] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Takashi Toyao
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
| | - Zen Maeno
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
| | - Satoru Takakusagi
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
| | - Takashi Kamachi
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
- Department of Life, Environment and Materials Science, Fukuoka Institute of Technology, 3-30-1Wajiro-Higashi, Higashi-ku, Fukuoka 811-0295, Japan
| | - Ichigaku Takigawa
- RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
| | - Ken-ichi Shimizu
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
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Blümich B. Low-field and benchtop NMR. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 306:27-35. [PMID: 31311709 DOI: 10.1016/j.jmr.2019.07.030] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 04/03/2019] [Accepted: 07/08/2019] [Indexed: 05/28/2023]
Abstract
NMR started at low field. Important discoveries like the first observation of NMR in condensed matter, the spin echo, NMR for chemical analysis, Fourier NMR spectroscopy, 2D NMR spectroscopy and magnetic resonance imaging happened at field strengths considered low today. With time the footprint of the NMR instruments at these field strengths shrunk from the laboratory floor to the tabletop. The first commercial tabletop NMR instruments were compact relaxometers for food analysis followed by mobile relaxometers for materials testing and oil-well exploration culminating in tabletop spectrometers for chemical analysis, capable of performing nearly the whole methodical portfolio of today's high-field instruments. The increasing sensitivity afforded by the lower noise of modern electronics and the unfolding richness of hyperpolarization scenarios along with detection schemes alternative to nuclear induction enable NMR at ultra-low field strengths down to zero applied field, where spin-spin couplings in local fields dominate the residual Zeeman interaction. Miniaturization and cost-reduction of NMR instruments outline current development goals along with the development of smart-phone-like apps to conduct standard NMR analyses.
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Affiliation(s)
- Bernhard Blümich
- Institut für Technische und Makromolekulare Chemie, RWTH Aachen University, Aachen, Germany.
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30
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de Almeida AF, Moreira R, Rodrigues T. Synthetic organic chemistry driven by artificial intelligence. Nat Rev Chem 2019. [DOI: 10.1038/s41570-019-0124-0] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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31
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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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32
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Advancing Drug Discovery via Artificial Intelligence. Trends Pharmacol Sci 2019; 40:592-604. [DOI: 10.1016/j.tips.2019.06.004] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 05/23/2019] [Accepted: 06/11/2019] [Indexed: 01/15/2023]
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33
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Davies IW. The digitization of organic synthesis. Nature 2019; 570:175-181. [DOI: 10.1038/s41586-019-1288-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 04/26/2019] [Indexed: 12/22/2022]
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34
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Häse F, Roch LM, Aspuru-Guzik A. Next-Generation Experimentation with Self-Driving Laboratories. TRENDS IN CHEMISTRY 2019. [DOI: 10.1016/j.trechm.2019.02.007] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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35
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Mattes DS, Jung N, Weber LK, Bräse S, Breitling F. Miniaturized and Automated Synthesis of Biomolecules-Overview and Perspectives. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2019; 31:e1806656. [PMID: 31033052 DOI: 10.1002/adma.201806656] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Revised: 02/02/2019] [Indexed: 06/09/2023]
Abstract
Chemical synthesis is performed by reacting different chemical building blocks with defined stoichiometry, while meeting additional conditions, such as temperature and reaction time. Such a procedure is especially suited for automation and miniaturization. Life sciences lead the way to synthesizing millions of different oligonucleotides in extremely miniaturized reaction sites, e.g., pinpointing active genes in whole genomes, while chemistry advances different types of automation. Recent progress in matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging could match miniaturized chemical synthesis with a powerful analytical tool to validate the outcome of many different synthesis pathways beyond applications in the life sciences. Thereby, due to the radical miniaturization of chemical synthesis, thousands of molecules can be synthesized. This in turn should allow ambitious research, e.g., finding novel synthesis routes or directly screening for photocatalysts. Herein, different technologies are discussed that might be involved in this endeavor. A special emphasis is given to the obstacles that need to be tackled when depositing tiny amounts of materials to many different extremely miniaturized reaction sites.
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Affiliation(s)
- Daniela S Mattes
- Institute of Microstructure Technology (IMT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Nicole Jung
- Institute of Organic Chemistry (IOC), Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 6, 76131, Karlsruhe, Germany
| | - Laura K Weber
- Institute of Microstructure Technology (IMT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Stefan Bräse
- Institute of Organic Chemistry (IOC), Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 6, 76131, Karlsruhe, Germany
| | - Frank Breitling
- Institute of Microstructure Technology (IMT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
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36
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Li YP, Han K, Grambow CA, Green WH. Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry. J Phys Chem A 2019; 123:2142-2152. [DOI: 10.1021/acs.jpca.8b10789] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Yi-Pei Li
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Kehang Han
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Colin A. Grambow
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - William H. Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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Vasudevan RK, Choudhary K, Mehta A, Smith R, Kusne G, Tavazza F, Vlcek L, Ziatdinov M, Kalinin SV, Hattrick-Simpers J. Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics. MRS COMMUNICATIONS 2019; 9:10.1557/mrc.2019.95. [PMID: 32166045 PMCID: PMC7067066 DOI: 10.1557/mrc.2019.95] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 07/03/2019] [Indexed: 05/14/2023]
Abstract
The use of advanced data analytics and applications of statistical and machine learning approaches ('AI') to materials science is experiencing explosive growth recently. In this prospective, we review recent work focusing on generation and application of libraries from both experiment and theoretical tools, across length scales. The available library data both enables classical correlative machine learning, and also opens the pathway for exploration of underlying causative physical behaviors. We highlight the key advances facilitated by this approach, and illustrate how modeling, macroscopic experiments and atomic-scale imaging can be combined to dramatically accelerate understanding and development of new material systems via a statistical physics framework. These developments point towards a data driven future wherein knowledge can be aggregated and used collectively, accelerating the advancement of materials science.
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Affiliation(s)
- Rama K. Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Kamal Choudhary
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Apurva Mehta
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025
| | - Ryan Smith
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Gilad Kusne
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Francesca Tavazza
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Lukas Vlcek
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Sergei V. Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Jason Hattrick-Simpers
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
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Li J, Lu Y, Xu Y, Liu C, Tu Y, Ye S, Liu H, Xie Y, Qian H, Zhu X. AIR-Chem: Authentic Intelligent Robotics for Chemistry. J Phys Chem A 2018; 122:9142-9148. [PMID: 30395457 DOI: 10.1021/acs.jpca.8b10680] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The new era with prosperous artificial intelligence (AI) and robotics technology is reshaping the materials discovery process in a more radical fashion. Here we present authentic intelligent robotics for chemistry (AIR-Chem), integrated with technological innovations in the AI and robotics fields, functionalized with modules including gradient descent-based optimization frameworks, multiple external field modulations, a real-time computer vision (CV) system, and automated guided vehicle (AGV) parts. AIR-Chem is portable and remotely controllable by cloud computing. AIR-Chem can learn the parametric procedures for given targets and carry on laboratory operations in standalone mode, with high reproducibility, precision, and availability for knowledge regeneration. Moreover, an improved nucleation theory of size focusing on inorganic perovskite quantum dots (IPQDs) is theoretically proposed and experimentally testified to by AIR-Chem. This work aims to boost the process of an unmanned chemistry laboratory from the synthesis of chemical materials to the analysis of physical chemical properties, and it provides a vivid demonstration for future chemistry reshaped by AI and robotics technology.
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Affiliation(s)
| | | | | | | | | | | | - Haochen Liu
- Department of Electrical and Electronic Engineering , Southern University of Science and Technology , Shenzhen , Guangdong 518055 , China
| | - Yi Xie
- Hefei National Laboratory for Physical Sciences at Microscale, Collaborative Innovation Center of Chemistry for Energy Materials , University of Science and Technology of China , Hefei , Anhui 230026 , China
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