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Song Z, Chen J, Cheng J, Chen G, Qi Z. Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications. Chem Rev 2024; 124:248-317. [PMID: 38108629 DOI: 10.1021/acs.chemrev.3c00223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.
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Affiliation(s)
- Zhen Song
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Jiahui Chen
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Jie Cheng
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guzhong Chen
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zhiwen Qi
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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2
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Zhou T, Gui C, Sun L, Hu Y, Lyu H, Wang Z, Song Z, Yu G. Energy Applications of Ionic Liquids: Recent Developments and Future Prospects. Chem Rev 2023; 123:12170-12253. [PMID: 37879045 DOI: 10.1021/acs.chemrev.3c00391] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
Ionic liquids (ILs) consisting entirely of ions exhibit many fascinating and tunable properties, making them promising functional materials for a large number of energy-related applications. For example, ILs have been employed as electrolytes for electrochemical energy storage and conversion, as heat transfer fluids and phase-change materials for thermal energy transfer and storage, as solvents and/or catalysts for CO2 capture, CO2 conversion, biomass treatment and biofuel extraction, and as high-energy propellants for aerospace applications. This paper provides an extensive overview on the various energy applications of ILs and offers some thinking and viewpoints on the current challenges and emerging opportunities in each area. The basic fundamentals (structures and properties) of ILs are first introduced. Then, motivations and successful applications of ILs in the energy field are concisely outlined. Later, a detailed review of recent representative works in each area is provided. For each application, the role of ILs and their associated benefits are elaborated. Research trends and insights into the selection of ILs to achieve improved performance are analyzed as well. Challenges and future opportunities are pointed out before the paper is concluded.
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Affiliation(s)
- Teng Zhou
- Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR 999077, China
- HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen 518048, China
| | - Chengmin Gui
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Longgang Sun
- Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, China
| | - Yongxin Hu
- Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, China
| | - Hao Lyu
- Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, China
| | - Zihao Wang
- Department for Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
| | - Zhen Song
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Gangqiang Yu
- Faculty of Environment and Life, Beijing University of Technology, 100 Ping Le Yuan, Chaoyang District, Beijing 100124, China
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Wu X, Liu Q, Zhao Y, Zhang L, Du J. Reaction Kinetic Model Considering the Solvation Effect Based on the FMO Theory and Deep Learning. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c01473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Xinyuan Wu
- Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian116024, China
| | - Qilei Liu
- Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian116024, China
| | - Yujing Zhao
- Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian116024, China
| | - Lei Zhang
- Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian116024, China
| | - Jian Du
- Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian116024, China
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Roth DM, Dunkel P, Kampwerth J, Jupke A. Beyond Partition Coefficients: Model-Based Solvent Screening in Extractive-Reaction Processes Considering Fluid Dynamics and Mass Transfer Limitations. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c01820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | - Philipp Dunkel
- AVT─Fluid Process Engineering, RWTH Aachen University, D-52074 Aachen, Germany
| | - Jan Kampwerth
- AVT─Fluid Process Engineering, RWTH Aachen University, D-52074 Aachen, Germany
| | - Andreas Jupke
- AVT─Fluid Process Engineering, RWTH Aachen University, D-52074 Aachen, Germany
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Han X, Zhang J, Yang YI, Zhang Z, Yang L, Gao YQ. Enhanced Sampling Simulation Reveals How Solvent Influences Chirogenesis of the Intra-Molecular Diels-Alder Reaction. J Chem Theory Comput 2022; 18:4318-4326. [PMID: 35666128 DOI: 10.1021/acs.jctc.2c00233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The timescale involved in chemical reactions is quite often beyond that of normal molecular dynamics simulations. Here, we combine metadynamics with selective integrated tempering sampling to simulate an intra-molecular Diels-Alder reaction in explicit solvents. Based on a one-dimensional collective variable obtained from harmonic linear discriminant analysis, four chiral isomers of products were observed in the simulation. Analyses of reactive trajectories showed that this reaction follows a concerted mechanism in all four solvents. In addition, the hydrogen bond between the reactant and water solvent plays an important role in the water-accelerated reaction mechanism. The dynamics of chirality formation varies significantly with solvents. The chirality of products forms significantly before the transition state, especially in ionic liquid.
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Affiliation(s)
- Xu Han
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jun Zhang
- Changping Laboratory, Beijing 102206, China
| | - Yi Isaac Yang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 518132, China
| | - Zhen Zhang
- School of Physics and Technology, Tangshan Normal University, Tangshan 063000, China
| | - Lijiang Yang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yi Qin Gao
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.,Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 518132, China
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Pistikopoulos EN, Barbosa-Povoa A, Lee JH, Misener R, Mitsos A, Reklaitis GV, Venkatasubramanian V, You F, Gani R. Process systems engineering – The generation next? Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107252] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Adjiman CS, Sahinidis NV, Vlachos DG, Bakshi B, Maravelias CT, Georgakis C. Process Systems Engineering Perspective on the Design of Materials and Molecules. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.0c05399] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Claire S. Adjiman
- Department of Chemical Engineering, Centre for Process Systems Engineering and Institute for Molecular Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K
| | - Nikolaos V. Sahinidis
- H. Milton Stewart School of Industrial & Systems Engineering and School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Dionisios G. Vlachos
- Department of Chemical and Biomolecular Engineering, Catalysis Center for Energy Innovation, RAPID Manufacturing Institute, and Delaware Energy Institute (DEI), University of Delaware, Newark, Delaware 19716, United States
| | - Bhavik Bakshi
- Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States
| | - Christos T. Maravelias
- Department of Chemical & Biological Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
| | - Christos Georgakis
- Department of Chemical and Biological Engineering Systems Research Institute of Chemical and Biological Processes, Tufts University, Medford, Massachusetts 02155, United States
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8
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Computational design of heterogeneous catalysts and gas separation materials for advanced chemical processing. Front Chem Sci Eng 2020. [DOI: 10.1007/s11705-020-1959-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
AbstractFunctional materials are widely used in chemical industry in order to reduce the process cost while simultaneously increase the product quality. Considering their significant effects, systematic methods for the optimal selection and design of materials are essential. The conventional synthesis-and-test method for materials development is inefficient and costly. Additionally, the performance of the resulting materials is usually limited by the designer’s expertise. During the past few decades, computational methods have been significantly developed and they now become a very important tool for the optimal design of functional materials for various chemical processes. This article selectively focuses on two important process functional materials, namely heterogeneous catalyst and gas separation agent. Theoretical methods and representative works for computational screening and design of these materials are reviewed.
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Gertig C, Fleitmann L, Schilling J, Leonhard K, Bardow A. Rx‐COSMO‐CAMPD: Enhancing Reactions by Integrated Computer‐Aided Design of Solvents and Processes based on Quantum Chemistry. CHEM-ING-TECH 2020. [DOI: 10.1002/cite.202000112] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Christoph Gertig
- RWTH Aachen University Institute of Technical Thermodynamics Schinkelstraße 8 52062 Aachen Germany
| | - Lorenz Fleitmann
- RWTH Aachen University Institute of Technical Thermodynamics Schinkelstraße 8 52062 Aachen Germany
| | - Johannes Schilling
- RWTH Aachen University Institute of Technical Thermodynamics Schinkelstraße 8 52062 Aachen Germany
| | - Kai Leonhard
- RWTH Aachen University Institute of Technical Thermodynamics Schinkelstraße 8 52062 Aachen Germany
| | - André Bardow
- RWTH Aachen University Institute of Technical Thermodynamics Schinkelstraße 8 52062 Aachen Germany
- Forschungszentrum Jülich GmbH Institute of Energy and Climate Research – Energy Systems Engineering (IEK-10) Wilhelm-Johnen-Straße 52425 Jülich Germany
- ETH Zurich Department of Mechanical and Process Engineering, Energy & Process Systems Engineering Tannenstrasse 3 8092 Zürich Switzerland
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10
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Alshehri AS, Gani R, You F. Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107005] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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11
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Fujinami M, Maekawara H, Isshiki R, Seino J, Yamaguchi J, Nakai H. Solvent Selection Scheme Using Machine Learning Based on Physicochemical Description of Solvent Molecules: Application to Cyclic Organometallic Reaction. BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2020. [DOI: 10.1246/bcsj.20200045] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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
| | - Hiroki Maekawara
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Ryota Isshiki
- Department of Applied Chemistry, 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 (WISE), Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Junichiro Yamaguchi
- Department of Applied Chemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, 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 (WISE), Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Elements Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Katsura, Kyoto 615-8520, Japan
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12
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13
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Zhang L, Mao H, Liu Q, Gani R. Chemical product design – recent advances and perspectives. Curr Opin Chem Eng 2020. [DOI: 10.1016/j.coche.2019.10.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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14
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Computer-aided molecular and processes design based on quantum chemistry: current status and future prospects. Curr Opin Chem Eng 2020. [DOI: 10.1016/j.coche.2019.11.007] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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15
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16
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Hu X, Qin H, Hu B, Cheng H, Chen L, Qi Z. A rate-based method for dynamic analysis and optimal design of reactive extraction: n-Hexyl acetate esterification as an example. Chin J Chem Eng 2020. [DOI: 10.1016/j.cjche.2019.04.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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17
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Liu Q, Zhang L, Tang K, Feng Y, Zhang J, Zhuang Y, Liu L, Du J. Computer-aided reaction solvent design considering inertness using group contribution-based reaction thermodynamic model. Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2019.09.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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18
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Gertig C, Kröger L, Fleitmann L, Scheffczyk J, Bardow A, Leonhard K. Rx-COSMO-CAMD: Computer-Aided Molecular Design of Reaction Solvents Based on Predictive Kinetics from Quantum Chemistry. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b03232] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Christoph Gertig
- Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstraße 8, 52062 Aachen, Germany
| | - Leif Kröger
- Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstraße 8, 52062 Aachen, Germany
| | - Lorenz Fleitmann
- Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstraße 8, 52062 Aachen, Germany
| | - Jan Scheffczyk
- Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstraße 8, 52062 Aachen, Germany
| | - André Bardow
- Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstraße 8, 52062 Aachen, Germany
- Institute of Energy and Climate Research—Energy Systems Engineering (IEK-10), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
| | - Kai Leonhard
- Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstraße 8, 52062 Aachen, Germany
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19
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Simon LL, Kiss AA, Cornevin J, Gani R. Process engineering advances in pharmaceutical and chemical industries: digital process design, advanced rectification, and continuous filtration. Curr Opin Chem Eng 2019. [DOI: 10.1016/j.coche.2019.02.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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20
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Developing non-linear rate constant QSPR using decision trees and multi-gene genetic programming. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.05.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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21
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Tsichla A, Severins C, Gottfried M, Marquardt W. An Experimental Assessment of Model-Based Solvent Selection for Enhancing Reaction Kinetics. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b01040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Angeliki Tsichla
- Aachener Verfahrenstechnik−Process Systems Engineering, RWTH Aachen University, Forckenbeckstraße 51, 52074 Aachen, Germany
- Bayer Technology Services GmbH, 51368 Leverkusen, Germany
| | | | | | - Wolfgang Marquardt
- Aachener Verfahrenstechnik−Process Systems Engineering, RWTH Aachen University, Forckenbeckstraße 51, 52074 Aachen, Germany
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22
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Amar Y, Schweidtmann AM, Deutsch P, Cao L, Lapkin A. Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis. Chem Sci 2019; 10:6697-6706. [PMID: 31367324 PMCID: PMC6625492 DOI: 10.1039/c9sc01844a] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 05/28/2019] [Indexed: 12/19/2022] Open
Abstract
Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)2(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral α-β unsaturated γ-lactam. With two simultaneous objectives - high conversion and high diastereomeric excess - the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories.
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Affiliation(s)
- Yehia Amar
- Department of Chemical Engineering and Biotechnology , University of Cambridge , Philippa Fawcett Drive , Cambridge , CB3 0AS , UK .
| | - Artur M Schweidtmann
- Aachener Verfahrenstechnik - Process Systems Engineering , RWTH Aachen University , Aachen , Germany
| | - Paul Deutsch
- UCB Pharma S.A. Allée de la Recherche , 60 1070 , Brussels , Belgium
| | - Liwei Cao
- Department of Chemical Engineering and Biotechnology , University of Cambridge , Philippa Fawcett Drive , Cambridge , CB3 0AS , UK .
- Cambridge Centre for Advanced Research and Education in Singapore Ltd. , 1 Create Way, CREATE Tower #05-05 , 138602 , Singapore
| | - Alexei Lapkin
- Department of Chemical Engineering and Biotechnology , University of Cambridge , Philippa Fawcett Drive , Cambridge , CB3 0AS , UK .
- Cambridge Centre for Advanced Research and Education in Singapore Ltd. , 1 Create Way, CREATE Tower #05-05 , 138602 , Singapore
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24
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Liu Q, Zhang L, Liu L, Du J, Tula AK, Eden M, Gani R. OptCAMD: An optimization-based framework and tool for molecular and mixture product design. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.01.006] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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25
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Zhou T, Song Z, Zhang X, Gani R, Sundmacher K. Optimal Solvent Design for Extractive Distillation Processes: A Multiobjective Optimization-Based Hierarchical Framework. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b04245] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Teng Zhou
- Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
| | - Zhen Song
- Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
| | - Xiang Zhang
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Rafiqul Gani
- PSE for SPEED, Skyttemosen 6, DK 3450 Allerod, Denmark
| | - Kai Sundmacher
- Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
- Process Systems Engineering, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, D-39106 Magdeburg, Germany
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26
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Datta S, Dev VA, Eden MR. Hybrid genetic algorithm-decision tree approach for rate constant prediction using structures of reactants and solvent for Diels-Alder reaction. Comput Chem Eng 2017. [DOI: 10.1016/j.compchemeng.2017.02.022] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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27
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Austin ND, Sahinidis NV, Konstantinov IA, Trahan DW. COSMO-based computer-aided molecular/mixture design: A focus on reaction solvents. AIChE J 2017. [DOI: 10.1002/aic.15871] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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28
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Zhou T, Zhou Y, Sundmacher K. A hybrid stochastic–deterministic optimization approach for integrated solvent and process design. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2016.03.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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29
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Struebing H, Obermeier S, Siougkrou E, Adjiman CS, Galindo A. A QM-CAMD approach to solvent design for optimal reaction rates. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2016.09.032] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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30
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Ten JY, Hassim MH, Ng DKS, Chemmangattuvalappil NG. A molecular design methodology by the simultaneous optimisation of performance, safety and health aspects. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2016.03.026] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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31
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Schilling J, Lampe M, Gross J, Bardow A. 1-stage CoMT-CAMD: An approach for integrated design of ORC process and working fluid using PC-SAFT. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2016.04.048] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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32
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Austin ND, Sahinidis NV, Trahan DW. Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques. Chem Eng Res Des 2016. [DOI: 10.1016/j.cherd.2016.10.014] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Scheffczyk J, Redepenning C, Jens CM, Winter B, Leonhard K, Marquardt W, Bardow A. Massive, automated solvent screening for minimum energy demand in hybrid extraction–distillation using COSMO-RS. Chem Eng Res Des 2016. [DOI: 10.1016/j.cherd.2016.09.029] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Zhou T, Wang J, McBride K, Sundmacher K. Optimal design of solvents for extractive reaction processes. AIChE J 2016. [DOI: 10.1002/aic.15360] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Teng Zhou
- Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems; Sandtorstr. 1 D-39106 Magdeburg Germany
| | - Jiayuan Wang
- Process Systems Engineering, Otto-von-Guericke University Magdeburg; Universitätsplatz 2 D-39106 Magdeburg Germany
| | - Kevin McBride
- Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems; Sandtorstr. 1 D-39106 Magdeburg Germany
| | - Kai Sundmacher
- Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems; Sandtorstr. 1 D-39106 Magdeburg Germany
- Process Systems Engineering, Otto-von-Guericke University Magdeburg; Universitätsplatz 2 D-39106 Magdeburg Germany
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Reif MM, Hünenberger PH. Origin of Asymmetric Solvation Effects for Ions in Water and Organic Solvents Investigated Using Molecular Dynamics Simulations: The Swain Acity-Basity Scale Revisited. J Phys Chem B 2016; 120:8485-517. [PMID: 27173101 DOI: 10.1021/acs.jpcb.6b02156] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The asymmetric solvation of ions can be defined as the tendency of a solvent to preferentially solvate anions over cations or cations over anions, at identical ionic charge magnitudes and effective sizes. Taking water as a reference, these effects are quantified experimentally for many solvents by the relative acity (A) and basity (B) parameters of the Swain scale. The goal of the present study is to investigate the asymmetric solvation of ions using molecular dynamics simulations, and to connect the results to this empirical scale. To this purpose, the charging free energies of alkali and halide ions, and of their hypothetical oppositely charged counterparts, are calculated in a variety of solvents. In a first set of calculations, artificial solvent models are considered that present either a charge or a shape asymmetry at the molecular level. The solvation asymmetry, probed by the difference in charging free energy between the two oppositely charged ions, is found to encompass a term quadratic in the ion charge, related to the different solvation structures around the anion and cation, and a term linear in the ion charge, related to the solvation structure around the uncharged ion-sized cavity. For these simple solvent models, the two terms are systematically counteracting each other, and it is argued that only the quadratic term should be retained when comparing the results of simulations involving physical solvents to experimental data. In a second set of calculations, 16 physical solvents are considered. The theoretical estimates for the acity A are found to correlate very well with the Swain parameters, whereas the correlation for B is very poor. Based on this observation, the Swain scale is reformulated into a new scale involving an asymmetry parameter Σ, positive for acitic solvents and negative for basitic ones, and a polarity parameter Π. This revised scale has the same predictive power as the original scale, but it characterizes asymmetry in an absolute sense, the atomistic simulations playing the role of an extra-thermodynamic assumption, and is optimally compatible with the simulation results. Considering the 55 solvents in the Swain set, it is observed that a moderate basity (Σ between -0.9 and -0.3, related to electronic polarization) represents the baseline for most solvents, while a highly variable acity (Σ between 0.0 and 3.0, related to hydrogen-bond donor capacity modulated by inductive effects) represents a landmark of protic solvents.
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Affiliation(s)
- Maria M Reif
- Physics Department (T38), Technische Universität München , D-85748 Garching, Germany
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