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Sato T, Masuda K, Sano C, Matsumoto K, Numata H, Munetoh S, Kasama T, Miyake R. Democratizing Microreactor Technology for Accelerated Discoveries in Chemistry and Materials Research. MICROMACHINES 2024; 15:1064. [PMID: 39337724 PMCID: PMC11434323 DOI: 10.3390/mi15091064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 08/11/2024] [Accepted: 08/14/2024] [Indexed: 09/30/2024]
Abstract
Microreactor technologies have emerged as versatile platforms with the potential to revolutionize chemistry and materials research, offering sustainable solutions to global challenges in environmental and health domains. This survey paper provides an in-depth review of recent advancements in microreactor technologies, focusing on their role in facilitating accelerated discoveries in chemistry and materials. Specifically, we examine the convergence of microfluidics with machine intelligence and automation, enabling the exploitation of the cyber-physical environment as a highly integrated experimentation platform for rapid scientific discovery and process development. We investigate the applicability and limitations of microreactor-enabled discovery accelerators in various chemistry and materials contexts. Despite their tremendous potential, the integration of machine intelligence and automation into microreactor-based experiments presents challenges in establishing fully integrated, automated, and intelligent systems. These challenges can hinder the broader adoption of microreactor technologies within the research community. To address this, we review emerging technologies that can help lower barriers and facilitate the implementation of microreactor-enabled discovery accelerators. Lastly, we provide our perspective on future research directions for democratizing microreactor technologies, with the aim of accelerating scientific discoveries and promoting widespread adoption of these transformative platforms.
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Affiliation(s)
- Tomomi Sato
- Graduate School of Engineering, The University of Tokyo, Kawasaki 212-0032, Japan; (T.K.); (R.M.)
| | - Koji Masuda
- Department of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, UK;
| | - Chikako Sano
- IBM Semiconductors, IBM Research–Tokyo, Kawasaki 212-0032, Japan; (C.S.); (K.M.); (H.N.); (S.M.)
| | - Keiji Matsumoto
- IBM Semiconductors, IBM Research–Tokyo, Kawasaki 212-0032, Japan; (C.S.); (K.M.); (H.N.); (S.M.)
| | - Hidetoshi Numata
- IBM Semiconductors, IBM Research–Tokyo, Kawasaki 212-0032, Japan; (C.S.); (K.M.); (H.N.); (S.M.)
| | - Seiji Munetoh
- IBM Semiconductors, IBM Research–Tokyo, Kawasaki 212-0032, Japan; (C.S.); (K.M.); (H.N.); (S.M.)
| | - Toshihiro Kasama
- Graduate School of Engineering, The University of Tokyo, Kawasaki 212-0032, Japan; (T.K.); (R.M.)
| | - Ryo Miyake
- Graduate School of Engineering, The University of Tokyo, Kawasaki 212-0032, Japan; (T.K.); (R.M.)
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Kirkland JK, Kumawat J, Shaban Tameh M, Tolman T, Lambert AC, Lief GR, Yang Q, Ess DH. Machine Learning Models for Predicting Zirconocene Properties and Barriers. J Chem Inf Model 2024; 64:775-784. [PMID: 38259142 DOI: 10.1021/acs.jcim.3c01575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Zr metallocenes have significant potential to be highly tunable polyethylene catalysts through modification of the aromatic ligand framework. Here we report the development of multiple machine learning models using a large library (>700 systems) of DFT-calculated zirconocene properties and barriers for ethylene polymerization. We show that very accurate machine learning models are possible for HOMO-LUMO gaps of precatalysts but the performance significantly depends on the machine learning algorithm and type of featurization, such as fingerprints, Coulomb matrices, smooth overlap of atomic positions, or persistence images. Surprisingly, the description of the bonding hapticity, the number of direct connections between Zr and the ligand aromatic carbons, only has a moderate influence on the performance of most models. Despite robust models for HOMO-LUMO gaps, these types of machine learning models based on structure connectivity type features perform poorly in predicting ethylene migratory insertion barrier heights. Therefore, we developed several relatively robust and accurate machine learning models for barrier heights that are based on quantum-chemical descriptors (QCDs). The quantitative accuracy of these models depends on which potential energy surface structure QCDs were harvested from. This revealed a Hammett-type principle to naturally emerge showing that QCDs from the π-coordination complexes provide much better descriptions of the transition states than other potential-energy structures. Feature importance analysis of the QCDs provides several fundamental principles that influence zirconocene catalyst reactivity.
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Affiliation(s)
- Justin K Kirkland
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Jugal Kumawat
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Maliheh Shaban Tameh
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Tyson Tolman
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Allison C Lambert
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Graham R Lief
- Research and Technology, Chevron Phillips Chemical Company, Highways 60 & 123, Bartlesville, Oklahoma 74003, United States
| | - Qing Yang
- Research and Technology, Chevron Phillips Chemical Company, Highways 60 & 123, Bartlesville, Oklahoma 74003, United States
| | - Daniel H Ess
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
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Duan C, Nandy A, Kulik HJ. Machine Learning for the Discovery, Design, and Engineering of Materials. Annu Rev Chem Biomol Eng 2022; 13:405-429. [PMID: 35320698 DOI: 10.1146/annurev-chembioeng-092320-120230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based modes, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. 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)
- Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , , .,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , , .,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , ,
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Ishola NB, McKenna TFL. Influence of Process Parameters on the Gas Phase Polymerization of Ethylene: RSM or ANN Statistical Methods? MACROMOL THEOR SIMUL 2021. [DOI: 10.1002/mats.202100059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Niyi B. Ishola
- CP2M‐UMR 5128 Université de Lyon Bâtiment CPE‐Lyon 43 Blvd du 11 Novembre 1918 Villeurbanne F‐69616 France
| | - Timothy F. L. McKenna
- CP2M‐UMR 5128 Université de Lyon Bâtiment CPE‐Lyon 43 Blvd du 11 Novembre 1918 Villeurbanne F‐69616 France
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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization. Front Chem Sci Eng 2021. [DOI: 10.1007/s11705-021-2083-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Nandy A, Duan C, Taylor MG, Liu F, Steeves AH, Kulik HJ. Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning. Chem Rev 2021; 121:9927-10000. [PMID: 34260198 DOI: 10.1021/acs.chemrev.1c00347] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.
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Affiliation(s)
- Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Michael G Taylor
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Fang Liu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Adam H Steeves
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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Malinowski J, Jacewicz D, Sikorski A, Urbaniak M, Rybiński P, Parnicka P, Zaleska-Medynska A, Gawdzik B, Drzeżdżon J. Cat-CrNP as new material with catalytic properties for 2-chloro-2-propen-1-ol and ethylene oligomerizations. Sci Rep 2021; 11:15212. [PMID: 34312412 PMCID: PMC8313536 DOI: 10.1038/s41598-021-94056-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 06/28/2021] [Indexed: 11/22/2022] Open
Abstract
The contemporary search for new catalysts for olefin oligomerization and polymerization is based on the study of coordinating compounds and/or organometallic compounds as post-metallocene catalysts. However known catalysts are suffered by many flaws, among others unsatisfactory activity, requirement of high pressure or instability at high temperatures. In this paper, we present a new catalyst i.e. the crystalline complex compound possesing high catalytic activity in the oligomerization of olefins, such as 2-chloro-2-propen-1-ol and ethylene under very mild conditions (room temperature, 0.12 bar for ethylene oligomerization, atmospheric pressure for 2-chloro-2-propen-1-ol oligomerization). New material—Cat-CrNP ([nitrilotriacetato-1,10-phenanthroline]chromium(III) tetrahydrate) has been obtained as crystalline form of the nitrilotriacetate complex compound of chromium(III) with 1,10-phenanthroline and characterized in terms of its crystal structure by the XRD method and by multi-analytical investigations towards its physicochemical propeties The yield of catalytic oligomerization over Cat-CrNP reached to 213.92 g · mmol−1 · h−1· bar−1 and 3232 g · mmol−1 · h−1 · bar−1 for the 2-chloro-2-propen-1-ol and ethylene, respectively. Furthemore, the synthesis of Cat-CrNP is cheap, easy to perform and solvents used during preparation are environmentally friendly.
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Affiliation(s)
- Jacek Malinowski
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland
| | - Dagmara Jacewicz
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland
| | - Artur Sikorski
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland
| | - Mariusz Urbaniak
- Institute of Chemistry, Jan Kochanowski University, Swietokrzyska 15 G, 25-406, Kielce, Poland
| | - Przemysław Rybiński
- Institute of Chemistry, Jan Kochanowski University, Swietokrzyska 15 G, 25-406, Kielce, Poland
| | - Patrycja Parnicka
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland
| | | | - Barbara Gawdzik
- Institute of Chemistry, Jan Kochanowski University, Swietokrzyska 15 G, 25-406, Kielce, Poland.
| | - Joanna Drzeżdżon
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland
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9
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Mapping the Structure–Property Space of Bimodal Polyethylene Using Response Surface Methods. Part 2: Experimental Investigation of Polymer Microstructure and Yield Estimations. MACROMOL REACT ENG 2020. [DOI: 10.1002/mren.202000023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Maley SM, Kwon DH, Rollins N, Stanley JC, Sydora OL, Bischof SM, Ess DH. Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization. Chem Sci 2020; 11:9665-9674. [PMID: 34094231 PMCID: PMC8161675 DOI: 10.1039/d0sc03552a] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 08/20/2020] [Indexed: 12/20/2022] Open
Abstract
The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here, we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene : 1-octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr-N distance, Cr-α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene.
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Affiliation(s)
- Steven M Maley
- Department of Chemistry and Biochemistry, Brigham Young University Provo Utah 84602 USA
| | - Doo-Hyun Kwon
- Department of Chemistry and Biochemistry, Brigham Young University Provo Utah 84602 USA
| | - Nick Rollins
- Department of Chemistry and Biochemistry, Brigham Young University Provo Utah 84602 USA
| | - Johnathan C Stanley
- Department of Chemistry and Biochemistry, Brigham Young University Provo Utah 84602 USA
| | - Orson L Sydora
- Research and Technology, Chevron Phillips Chemical Company LP 1862, Kingwood Drive Kingwood Texas 77339 USA
| | - Steven M Bischof
- Research and Technology, Chevron Phillips Chemical Company LP 1862, Kingwood Drive Kingwood Texas 77339 USA
| | - Daniel H Ess
- Department of Chemistry and Biochemistry, Brigham Young University Provo Utah 84602 USA
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Rizkin BA, Shkolnik AS, Ferraro NJ, Hartman RL. Combining automated microfluidic experimentation with machine learning for efficient polymerization design. NAT MACH INTELL 2020. [DOI: 10.1038/s42256-020-0166-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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12
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Rizkin BA, Hartman RL. Activation of homogenous polyolefin catalysis with a machine-assisted reactor laboratory-in-a-box (μAIR-LAB). REACT CHEM ENG 2020. [DOI: 10.1039/d0re00139b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Catalysis discovery is typically limited to specialized labs – this work demonstrates an Artificially Intelligent Microreactor Lab in a Box applied to investigate the chemistry of different co-catalysts for zirconocene-catalyzed olefin polymerization.
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Affiliation(s)
- Benjamin A. Rizkin
- New York University
- Department of Chemical and Biomolecular Engineering
- Brooklyn NY
- USA
| | - Ryan L. Hartman
- New York University
- Department of Chemical and Biomolecular Engineering
- Brooklyn NY
- USA
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Knox ST, Warren NJ. Enabling technologies in polymer synthesis: accessing a new design space for advanced polymer materials. REACT CHEM ENG 2020. [DOI: 10.1039/c9re00474b] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
This review discusses how developments in laboratory technologies can push the boundaries of what is achievable using existing polymer synthesis techniques.
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Affiliation(s)
- Stephen T. Knox
- School of Chemical and Process Engineering
- University of Leeds
- Leeds
- UK
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