1
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Saltão VAC, Thybaut JW, Pirro L, Freire FG, Mendes PSF. Automation of Data-Driven Rate Equation Screening for Heterogeneously Catalyzed Reactions. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c01920] [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)
- Vasco A. C. Saltão
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
- Chemical Engineering Department, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| | - Joris W. Thybaut
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
| | - Laura Pirro
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
| | - Filipe G. Freire
- Chemical Engineering Department, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| | - Pedro S. F. Mendes
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
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2
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Venkatasubramanian V, Mann V. Artificial intelligence in reaction prediction and chemical synthesis. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2021.100749] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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3
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Mendes PSF, Siradze S, Pirro L, Thybaut JW. Extracting kinetic information in catalysis: an automated tool for the exploration of small data. REACT CHEM ENG 2022. [DOI: 10.1039/d1re00215e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Kinetically relevant information for heterogeneously catalysed reactions is automatically extracted from small datasets by means of a newly-developed machine learning chemically-enriched tool.
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Affiliation(s)
- Pedro S. F. Mendes
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
| | - Sébastien Siradze
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
| | - Laura Pirro
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
| | - Joris W. Thybaut
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
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4
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Gupta U, Vlachos DG. Learning Chemistry of Complex Reaction Systems via a Python First-Principles Reaction Rule Stencil (pReSt) Generator. J Chem Inf Model 2021; 61:3431-3441. [PMID: 34265203 DOI: 10.1021/acs.jcim.1c00297] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Complex reaction networks can be generated with automated network generators from initial reactants and reaction rules. Reaction rule specification is central to network generation. These reaction rules are, at present, user-defined based on (intuitive) expert knowledge of chemistry and are often transferred from gas-phase to surface processes. The catalyst active site geometry is usually left out but is often responsible for selectivity. We propose a first-principles-based reaction mechanism generation framework using density functional theory (DFT) data of published reaction mechanisms. The framework "learns the chemistry" from published mechanisms. It can generate reaction networks not studied before, "flag" reactions not seen before for further DFT convergence tests, and easily reconcile differences between catalysts and reactants that may introduce new pathways never seen before. As such, it can be a diagnostic tool for data (mechanism) quality assessment and novel pathway discovery to new molecules. A software, the Python Reaction Stencil (pReSt), was developed for this purpose to wrap around automatic mechanism generation software. Multiple catalytic chemistries are considered to show the efficacy of the proposed framework.
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Affiliation(s)
- Udit Gupta
- Department of Chemical and Biomolecular Engineering, Rapid Advancement in Process Intensification Deployment (RAPID) Institute, Delaware Energy Institute, University of Delaware, Newark, Delaware 19716, United States
| | - Dionisios G Vlachos
- Department of Chemical and Biomolecular Engineering, Rapid Advancement in Process Intensification Deployment (RAPID) Institute, Delaware Energy Institute, University of Delaware, Newark, Delaware 19716, United States
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5
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Identification of Granule Growth Regimes in High Shear Wet Granulation Processes Using a Physics-Constrained Neural Network. Processes (Basel) 2021. [DOI: 10.3390/pr9050737] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The digitization of manufacturing processes has led to an increase in the availability of process data, which has enabled the use of data-driven models to predict the outcomes of these manufacturing processes. Data-driven models are instantaneous in simulate and can provide real-time predictions but lack any governing physics within their framework. When process data deviates from original conditions, the predictions from these models may not agree with physical boundaries. In such cases, the use of first-principle-based models to predict process outcomes have proven to be effective but computationally inefficient and cannot be solved in real time. Thus, there remains a need to develop efficient data-driven models with a physical understanding about the process. In this work, we have demonstrate the addition of physics-based boundary conditions constraints to a neural network to improve its predictability for granule density and granule size distribution (GSD) for a high shear granulation process. The physics-constrained neural network (PCNN) was better at predicting granule growth regimes when compared to other neural networks with no physical constraints. When input data that violated physics-based boundaries was provided, the PCNN identified these points more accurately compared to other non-physics constrained neural networks, with an error of <1%. A sensitivity analysis of the PCNN to the input variables was also performed to understand individual effects on the final outputs.
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6
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Venkatasubramanian V. Editorial. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2020.107176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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7
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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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8
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Corrigan N, Ciftci M, Jung K, Boyer C. Gesteuerte Reaktionsorthogonalität in der Polymer‐ und Materialwissenschaft. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.201912001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Nathaniel Corrigan
- Centre for Advanced Macromolecular Design and Australian Centre for Nanomedicine School of Chemical Engineering UNSW Sydney 2052 Australia
| | - Mustafa Ciftci
- Centre for Advanced Macromolecular Design and Australian Centre for Nanomedicine School of Chemical Engineering UNSW Sydney 2052 Australia
- Department of Chemistry Faculty of Engineering and Natural Science Bursa Technical University Bursa 16310 Turkey
| | - Kenward Jung
- Centre for Advanced Macromolecular Design and Australian Centre for Nanomedicine School of Chemical Engineering UNSW Sydney 2052 Australia
| | - Cyrille Boyer
- Centre for Advanced Macromolecular Design and Australian Centre for Nanomedicine School of Chemical Engineering UNSW Sydney 2052 Australia
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9
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Corrigan N, Ciftci M, Jung K, Boyer C. Mediating Reaction Orthogonality in Polymer and Materials Science. Angew Chem Int Ed Engl 2020; 60:1748-1781. [DOI: 10.1002/anie.201912001] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Indexed: 12/20/2022]
Affiliation(s)
- Nathaniel Corrigan
- Centre for Advanced Macromolecular Design and Australian Centre for Nanomedicine School of Chemical Engineering UNSW Sydney 2052 Australia
| | - Mustafa Ciftci
- Centre for Advanced Macromolecular Design and Australian Centre for Nanomedicine School of Chemical Engineering UNSW Sydney 2052 Australia
- Department of Chemistry Faculty of Engineering and Natural Science Bursa Technical University Bursa 16310 Turkey
| | - Kenward Jung
- Centre for Advanced Macromolecular Design and Australian Centre for Nanomedicine School of Chemical Engineering UNSW Sydney 2052 Australia
| | - Cyrille Boyer
- Centre for Advanced Macromolecular Design and Australian Centre for Nanomedicine School of Chemical Engineering UNSW Sydney 2052 Australia
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10
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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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11
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Timoshenko J, Frenkel AI. “Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors. ACS Catal 2019. [DOI: 10.1021/acscatal.9b03599] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Janis Timoshenko
- Department of Interface Science, Fritz-Haber-Institute of the Max Planck Society, 14195 Berlin, Germany
| | - Anatoly I. Frenkel
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States
- Chemistry Division, Brookhaven National Laboratory, Upton, New York 11973, United States
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12
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Janet JP, Duan C, Yang T, Nandy A, Kulik HJ. A quantitative uncertainty metric controls error in neural network-driven chemical discovery. Chem Sci 2019; 10:7913-7922. [PMID: 31588334 PMCID: PMC6764470 DOI: 10.1039/c9sc02298h] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Accepted: 07/11/2019] [Indexed: 12/14/2022] Open
Abstract
Machine learning (ML) models, such as artificial neural networks, have emerged as a complement to high-throughput screening, enabling characterization of new compounds in seconds instead of hours. The promise of ML models to enable large-scale chemical space exploration can only be realized if it is straightforward to identify when molecules and materials are outside the model's domain of applicability. Established uncertainty metrics for neural network models are either costly to obtain (e.g., ensemble models) or rely on feature engineering (e.g., feature space distances), and each has limitations in estimating prediction errors for chemical space exploration. We introduce the distance to available data in the latent space of a neural network ML model as a low-cost, quantitative uncertainty metric that works for both inorganic and organic chemistry. The calibrated performance of this approach exceeds widely used uncertainty metrics and is readily applied to models of increasing complexity at no additional cost. Tightening latent distance cutoffs systematically drives down predicted model errors below training errors, thus enabling predictive error control in chemical discovery or identification of useful data points for active learning.
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Affiliation(s)
- Jon Paul Janet
- Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA . ; Tel: +1-617-253-4584
| | - Chenru Duan
- Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA . ; Tel: +1-617-253-4584
- Department of Chemistry , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA
| | - Tzuhsiung Yang
- Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA . ; Tel: +1-617-253-4584
| | - Aditya Nandy
- Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA . ; Tel: +1-617-253-4584
- Department of Chemistry , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA
| | - Heather J Kulik
- Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA . ; Tel: +1-617-253-4584
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13
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Suzuki K, Toyao T, Maeno Z, Takakusagi S, Shimizu K, Takigawa I. Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data. ChemCatChem 2019. [DOI: 10.1002/cctc.201900971] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Keisuke Suzuki
- Graduate School of Information Science and Technology Hokkaido University Sapporo 001-0021 Japan
| | - Takashi Toyao
- Institute for Catalysis Hokkaido University Sapporo 001-0021 Japan
- Elements Strategy Initiative for Catalysis and Batteries Kyoto University Kyoto 615-8520 Japan
| | - Zen Maeno
- Institute for Catalysis Hokkaido University Sapporo 001-0021 Japan
| | | | - Ken‐ichi Shimizu
- Institute for Catalysis Hokkaido University Sapporo 001-0021 Japan
- Elements Strategy Initiative for Catalysis and Batteries Kyoto University Kyoto 615-8520 Japan
| | - Ichigaku Takigawa
- RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD) Hokkaido University Sapporo 001-0021 Japan
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14
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Vernuccio S, Broadbelt LJ. Discerning complex reaction networks using automated generators. AIChE J 2019. [DOI: 10.1002/aic.16663] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Sergio Vernuccio
- Department of Chemical and Biological Engineering Northwestern University Evanston Illinois
| | - Linda J. Broadbelt
- Department of Chemical and Biological Engineering Northwestern University Evanston Illinois
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15
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Pirro L, Mendes PSF, Paret S, Vandegehuchte BD, Marin GB, Thybaut JW. Descriptor–property relationships in heterogeneous catalysis: exploiting synergies between statistics and fundamental kinetic modelling. Catal Sci Technol 2019. [DOI: 10.1039/c9cy00719a] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Combined kinetic and statistical approach to shed light on the link between kinetically-relevant descriptors and easily tuneable catalyst properties.
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Affiliation(s)
- Laura Pirro
- Laboratory for Chemical Technology
- Ghent University
- B-9052 Ghent
- Belgium
| | | | - Stijn Paret
- Laboratory for Chemical Technology
- Ghent University
- B-9052 Ghent
- Belgium
| | | | - Guy B. Marin
- Laboratory for Chemical Technology
- Ghent University
- B-9052 Ghent
- Belgium
| | - Joris W. Thybaut
- Laboratory for Chemical Technology
- Ghent University
- B-9052 Ghent
- Belgium
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16
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Venkatasubramanian V. The promise of artificial intelligence in chemical engineering: Is it here, finally? AIChE J 2018. [DOI: 10.1002/aic.16489] [Citation(s) in RCA: 271] [Impact Index Per Article: 45.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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17
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Kim JY, Kulik HJ. When Is Ligand pKa a Good Descriptor for Catalyst Energetics? In Search of Optimal CO2 Hydration Catalysts. J Phys Chem A 2018; 122:4579-4590. [DOI: 10.1021/acs.jpca.8b03301] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Jeong Yun Kim
- 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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18
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Koledina KF, Koledin SN, Schadneva NA, Mayakova YY, Gubaydullin IM. Kinetic model of the catalytic reaction of dimethylcarbonate with alcohols in the presence Co2(CO)8 and W(CO)6. REACTION KINETICS MECHANISMS AND CATALYSIS 2017. [DOI: 10.1007/s11144-017-1181-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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19
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Water activation and carbon monoxide coverage effects on maximum rates for low temperature water-gas shift catalysis. J Catal 2017. [DOI: 10.1016/j.jcat.2017.01.016] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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20
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Kinetic model of the catalytic hydroalumination of olefins with organoaluminum compounds. REACTION KINETICS MECHANISMS AND CATALYSIS 2015. [DOI: 10.1007/s11144-015-0927-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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21
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Nurislamova LF, Gubaydullin IM, Koledina KF. Kinetic model of isolated reactions of the catalytic hydroalumination of olefins. REACTION KINETICS MECHANISMS AND CATALYSIS 2015. [DOI: 10.1007/s11144-015-0876-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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22
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Lauwaert J, De Canck E, Esquivel D, Van Der Voort P, Thybaut JW, Marin GB. Effects of amine structure and base strength on acid–base cooperative aldol condensation. Catal Today 2015. [DOI: 10.1016/j.cattod.2014.08.007] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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23
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Kechagiopoulos PN, Thybaut JW, Marin GB. Oxidative Coupling of Methane: A Microkinetic Model Accounting for Intraparticle Surface-Intermediates Concentration Profiles. Ind Eng Chem Res 2013. [DOI: 10.1021/ie403160s] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | - Joris W. Thybaut
- Ghent University, Laboratory for Chemical Technology, Technologiepark 914, B-9052 Ghent, Belgium
| | - Guy B. Marin
- Ghent University, Laboratory for Chemical Technology, Technologiepark 914, B-9052 Ghent, Belgium
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25
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de Souza L, Haida H, Thévenin D, Seidel-Morgenstern A, Janiga G. Model selection and parameter estimation for chemical reactions using global model structure. Comput Chem Eng 2013. [DOI: 10.1016/j.compchemeng.2013.07.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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26
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Efficient scheduling method of complex batch processes with general network structure via agent-based modeling. AIChE J 2013. [DOI: 10.1002/aic.14101] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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27
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28
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Prasad V, Karim AM, Arya A, Vlachos DG. Assessment of Overall Rate Expressions and Multiscale, Microkinetic Model Uniqueness via Experimental Data Injection: Ammonia Decomposition on Ru/γ-Al2O3 for Hydrogen Production. Ind Eng Chem Res 2009. [DOI: 10.1021/ie900144x] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- V. Prasad
- Department of Chemical Engineering and Center for Catalytic Science and Technology University of Delaware, Newark, Delaware 19716-3110
| | - A. M. Karim
- Department of Chemical Engineering and Center for Catalytic Science and Technology University of Delaware, Newark, Delaware 19716-3110
| | - A. Arya
- Department of Chemical Engineering and Center for Catalytic Science and Technology University of Delaware, Newark, Delaware 19716-3110
| | - D. G. Vlachos
- Department of Chemical Engineering and Center for Catalytic Science and Technology University of Delaware, Newark, Delaware 19716-3110
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29
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Thybaut JW, Choudhury IR, Denayer JF, Baron GV, Jacobs PA, Martens JA, Marin GB. Design of Optimum Zeolite Pore System for Central Hydrocracking of Long-Chain n-Alkanes based on a Single-Event Microkinetic Model. Top Catal 2009. [DOI: 10.1007/s11244-009-9274-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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30
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Venkatasubramanian V. DROWNING IN DATA: Informatics and modeling challenges in a data-rich networked world. AIChE J 2009. [DOI: 10.1002/aic.11756] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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31
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Hsu SH, Krishnamurthy B, Rao P, Zhao C, Jagannathan S, Venkatasubramanian V. A domain-specific compiler theory based framework for automated reaction network generation. Comput Chem Eng 2008. [DOI: 10.1016/j.compchemeng.2008.01.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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32
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Maurya M, Bornheimer S, Venkatasubramanian V, Subramaniam S. Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module. ACTA ACUST UNITED AC 2006; 152:229-42. [PMID: 16986265 PMCID: PMC3417759 DOI: 10.1049/ip-syb:20050014] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Biochemical systems embed complex networks and hence development and analysis of their detailed models pose a challenge for computation. Coarse-grained biochemical models, called reduced-order models (ROMs), consisting of essential biochemical mechanisms are more useful for computational analysis and for studying important features of a biochemical network. The authors present a novel method to model-reduction by identifying potentially important parameters using multidimensional sensitivity analysis. A ROM is generated for the GTPase-cycle module of m1 muscarinic acetylcholine receptor, Gq, and regulator of G-protein signalling 4 (a GTPase-activating protein or GAP) starting from a detailed model of 48 reactions. The resulting ROM has only 17 reactions. The ROM suggested that complexes of G-protein coupled receptor (GPCR) and GAP--which were proposed in the detailed model as a hypothesis--are required to fit the experimental data. Models previously published in the literature are also simulated and compared with the ROM. Through this comparison, a minimal ROM, that also requires complexes of GPCR and GAP, with just 15 parameters is generated. The proposed reduced-order modelling methodology is scalable to larger networks and provides a general framework for the reduction of models of biochemical systems.
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Affiliation(s)
- M.R. Maurya
- San Diego Supercomputer Center, 9500 Gilman Drive MC 0505, La Jolla, CA 92093, USA
| | - S.J. Bornheimer
- Departments of Chemistry and Biochemistry and Cellular and Molecular Medicine, University of California, San Diego, 9500 Gilman Drive La Jolla, CA 92093, USA
| | - V. Venkatasubramanian
- Laboratory for Intelligent Process Systems, School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - S. Subramaniam
- San Diego Supercomputer Center, 9500 Gilman Drive MC 0505, La Jolla, CA 92093, USA, the Departments of Chemistry and Biochemistry and Cellular and Molecular Medicine, University of California, San Diego, 9500 Gilman Drive La Jolla, CA 92093, USA and the Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive La Jolla, CA 92093, USA
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33
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Laurenzi IJ, Bartels JD, Diamond SL. Regression of Multicomponent Sticking Probabilities Using a Genetic Algorithm. Ind Eng Chem Res 2006. [DOI: 10.1021/ie051159t] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ian J. Laurenzi
- Department of Chemical Engineering, Lehigh University, Bethlehem, Pennsylvania 18015
| | - John D. Bartels
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Scott L. Diamond
- Department of Chemical Engineering, Institute for Medicine and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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Thybaut J, Laxmi Narasimhan C, Marin G. Bridging the gap between liquid and vapor phase hydrocracking. Catal Today 2006. [DOI: 10.1016/j.cattod.2005.10.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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A systematic approach for automated reaction network generation. ACTA ACUST UNITED AC 2006. [DOI: 10.1016/s1570-7946(06)80172-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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A systematic framework for the design of reduced-order models for signal transduction pathways from a control theoretic perspective. Comput Chem Eng 2006. [DOI: 10.1016/j.compchemeng.2005.10.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Schüth F. High-throughput experiments for synthesis and applications of zeolites. STUDIES IN SURFACE SCIENCE AND CATALYSIS 2005. [DOI: 10.1016/s0167-2991(05)80010-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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Katare S, Bhan A, Caruthers JM, Delgass WN, Venkatasubramanian V. A hybrid genetic algorithm for efficient parameter estimation of large kinetic models. Comput Chem Eng 2004. [DOI: 10.1016/j.compchemeng.2004.07.002] [Citation(s) in RCA: 71] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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