Quo vadis multiscale modeling in reaction engineering? – A perspective

The FAIR Guiding Principles for scientific data management and stewardship

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · cited 1× · see citing works

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517384113 · Cited by 4,796 · see citing works

Theoretical surface science and catalysis—calculations and concepts

Hammer, B., & Nørskov, J. K. (2000) · Advances in Catalysis · doi:10.1016/s0360-0564(02)45013-4 · Cited by 3,972 · see citing works

Insight on Tafel slopes from a microkinetic analysis of aqueous electrocatalysis for energy conversion

Shinagawa, T., Garcia-Esparza, A. T., & Takanabe, K. (2015) · Scientific Reports · doi:10.1038/srep13801 · Cited by 3,287 · see citing works

Control of metal-support interactions in heterogeneous catalysts to enhance activity and selectivity

van Deelen, T. W., Mejía, C. H., & de Jong, K. P. (2019) · Nature Catalysis · doi:10.1038/s41929-019-0364-x · Cited by 2,379 · see citing works

Chemical reactor analysis and design

Brooks, B. W. (1991) · The Chemical Engineering Journal · doi:10.1016/0300-9467(91)85016-o · Cited by 2,169 · see citing works

Response surface methodology

Khuri, A. I., & Mukhopadhyay, S. (2010) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.73 · Cited by 2,137 · see citing works

The Brønsted–Evans–Polanyi relation and the volcano curve in heterogeneous catalysis

Bligaard, T., Nørskov, J. K., Dahl, S., Matthiesen, J., Christensen, C. H., & Sehested, J. (2004) · Journal of Catalysis · doi:10.1016/j.jcat.2004.02.034 · Cited by 1,728 · see citing works

Scaling Properties of Adsorption Energies for Hydrogen-Containing Molecules on Transition-Metal Surfaces

Abild-Pedersen, F., Greeley, J., Studt, F., Rossmeisl, J., Munter, T. R., Moses, P. G., Skúlason, E., Bligaard, T., & Nørskov, J. K. (2007) · Physical Review Letters · doi:10.1103/physrevlett.99.016105 · Cited by 1,678 · see citing works

Detached-Eddy Simulation

Spalart, P. R. (2008) · Annual Review of Fluid Mechanics · doi:10.1146/annurev.fluid.010908.165130 · Cited by 1,615 · see citing works

Computationally efficient implementation of combustion chemistry using in situ adaptive tabulation

Pope, S. B. (1997) · Combustion Theory and Modelling · doi:10.1080/713665229 · Cited by 1,125 · see citing works

Use of DFT to achieve a rational understanding of acid?basic properties of ?-alumina surfaces

Digne, M. (2004) · Journal of Catalysis · doi:10.1016/j.jcat.2004.04.020 · Cited by 1,091 · see citing works

Heterogeneous Catalysis

Schlögl, R. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201410738 · Cited by 1,058 · see citing works

Review of discrete particle modeling of fluidized beds

Deen, N. G., Annaland, M. V. S., van der Hoef, M. A., & Kuipers, J. A. M. (2006) · Chemical Engineering Science · doi:10.1016/j.ces.2006.08.014 · Cited by 1,002 · see citing works

Machine Learning for Molecular Simulation

Noé, F., Tkatchenko, A., Müller, K.-R., & Clementi, C. (2020) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-042018-052331 · Cited by 956 · see citing works

Data-driven discovery of coordinates and governing equations

Champion, K., Lusch, B., Kutz, J. N., & Brunton, S. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906995116 · Cited by 909 · see citing works

Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms

Gao, C. W., Allen, J. W., Green, W. H., et al. (2016) · Computer Physics Communications · doi:10.1016/j.cpc.2016.02.013 · Cited by 866 · see citing works

Toward accommodating realistic fuel chemistry in large-scale computations

Lu, T., & Law, C. K. (2008) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2008.10.002 · Cited by 817 · see citing works

Open Catalyst 2020 (OC20) Dataset and Community Challenges

Chanussot, L., Das, A., Goyal, S., Lavril, T., Shuaibi, M., Rivière, M., Tran, K., Heras‐Domingo, J., Ho, C., Hu, W., et al. (2021) · ACS Catalysis · doi:10.1021/acscatal.0c04525 · Cited by 786 · see citing works

The 2020 plasma catalysis roadmap

Bogaerts, A., Tu, X., Whitehead, J. C., Centi, G., Lefferts, L., Guaitella, O., Azzolina-Jury, F., Kim, H., Murphy, A. B., Schneider, W. F., et al. (2020) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ab9048 · Cited by 658 · see citing works

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00004 · Cited by 636 · see citing works

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Ulissi, Z. W., Medford, A. J., Bligaard, T., & Nørskov, J. K. (2017) · Nature Communications · doi:10.1038/ncomms14621 · Cited by 614 · see citing works

Degree of Rate Control: How Much the Energies of Intermediates and Transition States Control Rates

Stegelmann, C., Andreasen, A., & Campbell, C. T. (2009) · Journal of the American Chemical Society · doi:10.1021/ja9000097 · Cited by 584 · see citing works

The Degree of Rate Control: A Powerful Tool for Catalysis Research

Campbell, C. T. (2017) · ACS Catalysis · doi:10.1021/acscatal.7b00115 · Cited by 578 · see citing works

First-principles theory of ionic diffusion with nondilute carriers

der Ven, A. V., Ceder, G., Asta, M., & Tepesch, P. D. (2001) · Physical review. B, Condensed matter · doi:10.1103/physrevb.64.184307 · Cited by 523 · see citing works

Microkinetic Modeling: A Tool for Rational Catalyst Design

Motagamwala, A. H., & Dumesic, J. A. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00394 · Cited by 488 · see citing works

Photocatalytic CO2 Reduction: A Review of Ab Initio Mechanism, Kinetics, and Multiscale Modeling Simulations

Kovačič, Ž., Likozar, B., & Huš, M. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c02557 · Cited by 481 · see citing works

Future Challenges in Heterogeneous Catalysis: Understanding Catalysts under Dynamic Reaction Conditions

Kalz, K. F., Kraehnert, R., Dvoyashkin, M., Dittmeyer, R., Gläser, R. J., Krewer, U., Reuter, K., & Grunwaldt, J. (2016) · ChemCatChem · doi:10.1002/cctc.201600996 · Cited by 459 · see citing works

Computational Methods in Heterogeneous Catalysis

Chen, B. W. J., Xu, L., & Mavrikakis, M. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01060 · Cited by 451 · see citing works

The UBI-QEP method: A practical theoretical approach to understanding chemistry on transition metal surfaces

Shustorovich, E. M. (1998) · Surface Science Reports · doi:10.1016/s0167-5729(97)00016-2 · Cited by 387 · see citing works

Assessing the reliability of calculated catalytic ammonia synthesis rates

Medford, A. J., Wellendorff, J., Vojvodic, A., Studt, F., Abild-Pedersen, F., Jacobsen, K. W., Bligaard, T., & Nørskov, J. K. (2014) · Science · doi:10.1126/science.1253486 · Cited by 377 · see citing works

A review of multiscale modeling of metal-catalyzed reactions: Mechanism development for complexity and emergent behavior

Salciccioli, M., Stamatakis, M., Caratzoulas, S., & Vlachos, D. G. (2011) · Chemical Engineering Science · doi:10.1016/j.ces.2011.05.050 · Cited by 367 · see citing works

A fully coupled computational fluid dynamics and multi-zone model with detailed chemical kinetics for the simulation of premixed charge compression ignition engines

Babajimopoulos, A., Assanis, D. N., Flowers, D. L., et al. (2005) · International Journal of Engine Research · doi:10.1243/146808705x30503 · Cited by 359 · see citing works

Machine Learning for Computational Heterogeneous Catalysis

Schlexer, P., Winther, K. T., Torres, J. A. G., Streibel, V., Zhao, M., Bajdich, M., Abild‐Pedersen, F., & Bligaard, T. (2019) · ChemCatChem · doi:10.1002/cctc.201900595 · Cited by 358 · see citing works

Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats

Dobbelaere, M. R., Plehiers, P. P., de Vijver, R. V., Stevens, C. V., & Geem, K. M. V. (2021) · Engineering · doi:10.1016/j.eng.2021.03.019 · Cited by 339 · see citing works

First-principles-based multiscale modelling of heterogeneous catalysis

Bruix, A., Margraf, J. T., Andersen, M., & Reuter, K. (2019) · Nature Catalysis · doi:10.1038/s41929-019-0298-3 · Cited by 338 · see citing works

A Practical Guide to Surface Kinetic Monte Carlo Simulations

Andersen, M., Panosetti, C., & Reuter, K. (2019) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00202 · Cited by 322 · see citing works

Overview of Surrogate Modeling in Chemical Process Engineering

McBride, K., & Sundmacher, K. (2019) · Chemie Ingenieur Technik · doi:10.1002/cite.201800091 · Cited by 319 · see citing works

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation

Liu, M., Dana, A. G., Johnson, M. S., et al. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01480 · Cited by 315 · see citing works

DEM-CFD simulations of fixed bed reactors with small tube to particle diameter ratios

Eppinger, T., Seidler, K., & Kraume, M. (2010) · Chemical Engineering Journal · doi:10.1016/j.cej.2010.10.053 · Cited by 294 · see citing works

Machine Learning in Chemical Engineering: A Perspective

Schweidtmann, A. M., Esche, E., Fischer, A., Kloft, M., Repke, J., Säger, S., & Mitsos, A. (2021) · Chemie Ingenieur Technik · doi:10.1002/cite.202100083 · Cited by 293 · see citing works

Ensembles of Metastable States Govern Heterogeneous Catalysis on Dynamic Interfaces

Zhang, Z., Zandkarimi, B., & Alexandrova, A. N. (2020) · Accounts of Chemical Research · doi:10.1021/acs.accounts.9b00531 · Cited by 283 · see citing works

Advances in fixed-bed reactor modeling using particle-resolved computational fluid dynamics (CFD)

Jurtz, N., Kraume, M., & Wehinger, G. D. (2018) · Reviews in Chemical Engineering · doi:10.1515/revce-2017-0059 · Cited by 275 · see citing works

Steam and dry reforming of methane on Rh: Microkinetic analysis and hierarchy of kinetic models

Maestri, M., Vlachos, D. G., Beretta, A., et al. (2008) · Journal of Catalysis · doi:10.1016/j.jcat.2008.08.008 · Cited by 262 · see citing works

ReacNetGenerator: an automatic reaction network generator for reactive molecular dynamics simulations

Zeng, J., Cao, L., Chin, C., Ren, H., Zhang, J. Z. H., & Zhu, T. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp05091d · Cited by 260 · see citing works

Methods for exploring reaction space in molecular systems

Dewyer, A. L., Argüelles, A. J., & Zimmerman, P. M. (2017) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1354 · Cited by 251 · see citing works

Combining Wave Function Methods with Density Functional Theory for Excited States

Ghosh, S., Verma, P., Cramer, C. J., Gagliardi, L., & Truhlar, D. G. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00193 · Cited by 251 · see citing works

The combustion of kerosene: Experimental results and kinetic modelling using 1- to 3-component surrogate model fuels

Dagaut, P., Bakali, A. E., & Ristori, A. (2005) · Fuel · doi:10.1016/j.fuel.2005.10.008 · Cited by 247 · see citing works

A graph-theoretical kinetic Monte Carlo framework for on-lattice chemical kinetics

Stamatakis, M., & Vlachos, D. G. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3596751 · Cited by 242 · see citing works

Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts

Back, S., Yoon, J., Tian, N., Zhong, W., Tran, K., & Ulissi, Z. W. (2019) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.9b01428 · Cited by 242 · see citing works

Gas transport in porous media: The dusty-gas model

Davies, C. (1984) · Journal of Aerosol Science · doi:10.1016/0021-8502(84)90058-2 · Cited by 234 · see citing works

A dynamic adaptive chemistry scheme for reactive flow computations

Liang, L., Stevens, J. G., & Farrell, J. T. (2009) · Proceedings of the Combustion Institute · doi:10.1016/j.proci.2008.05.073 · Cited by 231 · see citing works

Progress in Accurate Chemical Kinetic Modeling, Simulations, and Parameter Estimation for Heterogeneous Catalysis

Matera, S., Schneider, W. F., Heyden, A., & Savara, A. (2019) · ACS Catalysis · doi:10.1021/acscatal.9b01234 · Cited by 222 · see citing works

Roadmap on multiscale materials modeling

der Giessen, E. V., Schultz, P. A., Bertin, N., Bulatov, V. V., Wei Cai, A., Cśanyi, G., Foiles, S. M., Geers, M. G. D., González, C., Hütter, M., et al. (2020) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/1361-651x/ab7150 · Cited by 216 · see citing works

Handbook of Materials Modeling

Andreoni, W., & Yip, S. (2018) · Springer eBooks · doi:10.1007/978-3-319-50257-1 · Cited by 207 · see citing works

Computational Fluid Dynamics for Fixed Bed Reactor Design

Dixon, A. G., & Partopour, B. (2020) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092319-075328 · Cited by 207 · see citing works

Detailed numerical simulations of catalytic fixed-bed reactors: Heterogeneous dry reforming of methane

Wehinger, G. D., Eppinger, T., & Kraume, M. (2014) · Chemical Engineering Science · doi:10.1016/j.ces.2014.09.007 · Cited by 203 · see citing works

Oxide/water interfaces: how the surface chemistry modifies interfacial water properties

Gaigeot, M., Sprik, M., & Sulpizi, M. (2012) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/24/12/124106 · Cited by 193 · see citing works

Machine learning in chemical reaction space

Stocker, S., Cśanyi, G., Reuter, K., & Margraf, J. T. (2020) · Nature Communications · doi:10.1038/s41467-020-19267-x · Cited by 185 · see citing works

A modified Maxwell-Stefan model for transport through inert membranes: the binary friction model

Kerkhof, P. J. A. M. (1996) · The Chemical Engineering Journal and the Biochemical Engineering Journal · doi:10.1016/s0923-0467(96)03134-x · Cited by 184 · see citing works

Machine Learning for Atomic Simulation and Activity Prediction in Heterogeneous Catalysis: Current Status and Future

Ma, S., & Liu, Z. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c03472 · Cited by 182 · see citing works

Parallel kinetic Monte Carlo simulation framework incorporating accurate models of adsorbate lateral interactions

Nielsen, J. H., d’Avezac, M., Hetherington, J., & Stamatakis, M. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4840395 · Cited by 177 · see citing works

Thermodynamic Consistency in Microkinetic Development of Surface Reaction Mechanisms

Mhadeshwar, A. B., Wang, H., & Vlachos, D. G. (2003) · The Journal of Physical Chemistry B · doi:10.1021/jp034954y · Cited by 174 · see citing works

Quantum Chemical Modeling of Benzene Ethylation over H-ZSM-5 Approaching Chemical Accuracy: A Hybrid MP2:DFT Study

Hansen, N., Kerber, T., Sauer, J., Bell, A. T., & Keil, F. J. (2010) · Journal of the American Chemical Society · doi:10.1021/ja102261m · Cited by 171 · see citing works

Coarse-Grain DEM Modelling in Fluidized Bed Simulation: A Review

Renzo, A. D., Napolitano, E. S., & Maio, F. P. D. (2021) · Processes · doi:10.3390/pr9020279 · Cited by 167 · see citing works

Effects of correlated parameters and uncertainty in electronic-structure-based chemical kinetic modelling

Sutton, J. E., Guo, W., Katsoulakis, M. A., & Vlachos, D. G. (2016) · Nature Chemistry · doi:10.1038/nchem.2454 · Cited by 164 · see citing works

Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives

Guan, Y., Chaffart, D., Liu, G., Tan, Z., Zhang, D., Wang, Y., Li, J., & Ricardez‐Sandoval, L. (2021) · Chemical Engineering Science · doi:10.1016/j.ces.2021.117224 · Cited by 155 · see citing works

Revealing the Janus Character of the Coke Precursor in the Propane Direct Dehydrogenation on Pt Catalysts from a kMC Simulation

Lian, Z., Ali, S., Liu, T., et al. (2018) · ACS Catalysis · doi:10.1021/acscatal.8b00107 · Cited by 148 · see citing works

Response surface methodology for advanced manufacturing technology optimization: theoretical fundamentals, practical guidelines, and survey literature review

de Oliveira, L. G., de Paiva, A. P., Balestrassi, P. P., et al. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-03809-9 · Cited by 145 · see citing works

Connecting theory and simulation with experiment for the study of diffusion in nanoporous solids

Bukowski, B. C., Keil, F. J., Ravikovitch, P. I., Sastre, G., Snurr, R. Q., & Coppens, M. (2021) · Adsorption · doi:10.1007/s10450-021-00314-y · Cited by 145 · see citing works

First-Principles Statistical Mechanics Study of the Stability of a Subnanometer Thin Surface Oxide in Reactive Environments: CO Oxidation at Pd(100)

Rogal, J., Reuter, K., & Scheffler, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.046101 · Cited by 141 · see citing works

Coupling CFD with detailed microkinetic modeling in heterogeneous catalysis

Maestri, M., & Cuoci, A. (2013) · Chemical Engineering Science · doi:10.1016/j.ces.2013.03.048 · Cited by 138 · see citing works

Recent advances in knowledge discovery for heterogeneous catalysis using machine learning

Günay, M. E., & Yıldırım, R. (2020) · Catalysis Reviews · doi:10.1080/01614940.2020.1770402 · Cited by 136 · see citing works

Modelling of multi-component gas flows in capillaries and porous solids

Young, J. B., & Todd, B. (2005) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2005.07.034 · Cited by 135 · see citing works

Chemical reaction engineering: A multiscale approach to a multiobjective task

Lerou, J. J., & Ng, K. M. (1996) · Chemical Engineering Science · doi:10.1016/0009-2509(96)00022-x · Cited by 134 · see citing works

Hierarchical Multiscale Mechanism Development for Methane Partial Oxidation and Reforming and for Thermal Decomposition of Oxygenates on Rh

Mhadeshwar, A. B., & Vlachos, D. G. (2005) · The Journal of Physical Chemistry B · doi:10.1021/jp052479t · Cited by 133 · see citing works

Liquid-Phase Modeling in Heterogeneous Catalysis

Saleheen, M., & Heyden, A. (2018) · ACS Catalysis · doi:10.1021/acscatal.7b04367 · Cited by 130 · see citing works

Practical Models for Isothermal Diffusion and Flow of Gases in Porous Solids

Feng, C. F.-C., & Stewart, W. E. (1973) · Industrial & Engineering Chemistry Fundamentals · doi:10.1021/i160046a001 · Cited by 128 · see citing works

Combustion at a crossroads: Status and prospects

Law, C. K. (2006) · Proceedings of the Combustion Institute · doi:10.1016/j.proci.2006.08.124 · Cited by 125 · see citing works

Kinetic modelling of heterogeneous catalytic systems

Stamatakis, M. (2014) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/27/1/013001 · Cited by 123 · see citing works

Diffusion and reaction in porous networks

Keil, F. J. (1999) · Catalysis Today · doi:10.1016/s0920-5861(99)00119-4 · Cited by 122 · see citing works

Automatic Generation of Microkinetic Mechanisms for Heterogeneous Catalysis

Goldsmith, C. F., & West, R. H. (2017) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.7b02133 · Cited by 118 · see citing works

Modeling and control of crystal shape in continuous protein crystallization

Kwon, J. S., Nayhouse, M. J., Christofides, P. D., et al. (2013) · Chemical Engineering Science · doi:10.1016/j.ces.2013.12.005 · Cited by 112 · see citing works

Natural Gas Conversion in Monolithic Catalysts: Interaction of Chemical Reactions and Transport Phenomena

Deutschmann, O., Schwiedemoch, R., Maier, L. I., et al. (2001) · Studies in surface science and catalysis · doi:10.1016/s0167-2991(01)80312-8 · Cited by 109 · see citing works

A self-organizing-map approach to chemistry representation in combustion applications

Blasco, J., Fueyo, N., Dopazo, C., et al. (2000) · Combustion Theory and Modelling · doi:10.1088/1364-7830/4/1/304 · Cited by 108 · see citing works

Multiscale modelling and simulation: a position paper

Hoekstra, A. G., Bastien Chopard, B., & Coveney, P. V. (2014) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2013.0377 · Cited by 106 · see citing works

Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry

Rupp, M., von Lilienfeld, O. A., & Burke, K. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5043213 · Cited by 102 · see citing works

KMCLib: A general framework for lattice kinetic Monte Carlo (KMC) simulations

Leetmaa, M., & Skorodumova, N. V. (2014) · Computer Physics Communications · doi:10.1016/j.cpc.2014.04.017 · Cited by 100 · see citing works

Ab initio molecular dynamics with enhanced sampling in heterogeneous catalysis

Piccini, G., Lee, M., Yuk, S. F., Zhang, D., Collinge, G., Kollias, L., Nguyen, M., Glezakou, V., & Rousseau, R. J. (2021) · Catalysis Science & Technology · doi:10.1039/d1cy01329g · Cited by 98 · see citing works

Evaluating Catalytic Fixed‐Bed Reactors for Dry Reforming of Methane with Detailed CFD

Wehinger, G. D., Eppinger, T., & Kraume, M. (2015) · Chemie Ingenieur Technik · doi:10.1002/cite.201400153 · Cited by 96 · see citing works

Process modeling and optimization for biomass steam-gasification employing response surface methodology

Zaman, S. A., Roy, D., & Ghosh, S. (2020) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2020.105847 · Cited by 94 · see citing works

Predictive-Quality Surface Reaction Chemistry in Real Reactor Models: Integrating First-Principles Kinetic Monte Carlo Simulations into Computational Fluid Dynamics

Matera, S., Maestri, M., Cuoci, A., & Reuter, K. (2014) · ACS Catalysis · doi:10.1021/cs501154e · Cited by 92 · see citing works

Chemical product design – recent advances and perspectives

Zhang, L., Mao, H., Liu, Q., & Rafiqul Gani, A. (2019) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2019.10.005 · Cited by 91 · see citing works

Recent advances in numerical modeling of catalytic monolith reactors

Tischer, S., & Deutschmann, O. (2005) · Catalysis Today · doi:10.1016/j.cattod.2005.06.061 · Cited by 90 · see citing works

Assessment of Different Discrete Particle Methods Ability To Predict Gas-Particle Flow in a Small-Scale Fluidized Bed

Lu, L., Gopalan, B., & Benyahia, S. (2017) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.7b01862 · Cited by 84 · see citing works

Modeling and multi-objective optimization of variable air gasification performance parameters using Syzygium cumini biomass by integrating ASPEN Plus with Response surface methodology (RSM)

Singh, D. K., & Tirkey, J. V. (2021) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2021.03.054 · Cited by 82 · see citing works

Contact Modifications for CFD Simulations of Fixed-Bed Reactors: Cylindrical Particles

Wehinger, G. D., Fütterer, C., & Kraume, M. (2016) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.6b03596 · Cited by 81 · see citing works

A fundamental investigation of gas/solid mass transfer in open-cell foams using a combined experimental and CFD approach

Bracconi, M., Ambrosetti, M., Maestri, M., Groppi, G., & Tronconi, E. (2018) · Chemical Engineering Journal · doi:10.1016/j.cej.2018.07.023 · Cited by 81 · see citing works

Generalized Temporal Acceleration Scheme for Kinetic Monte Carlo Simulations of Surface Catalytic Processes by Scaling the Rates of Fast Reactions

Dybeck, E. C., Plaisance, C. P., & Neurock, M. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00859 · Cited by 81 · see citing works

Multi-Scale Simulation of Heterogeneous Surface Film Growth Mechanisms in Lithium-Ion Batteries

Röder, F., Braatz, R. D., & Krewer, U. (2017) · Journal of The Electrochemical Society · doi:10.1149/2.0241711jes · Cited by 81 · see citing works

Reactivity of shape-controlled crystals and metadynamics simulations locate the weak spots of alumina in water

Réocreux, R., Girel, E., Clabaut, P., Tuel, A., Besson, M., Chaumonnot, A., Cabiac, A., Sautet, P., & Michel, C. (2019) · Nature Communications · doi:10.1038/s41467-019-10981-9 · Cited by 81 · see citing works

A multiregion operator-splitting CFD approach for coupling microkinetic modeling with internal porous transport in heterogeneous catalytic reactors

Maffei, T., Gentile, G., Rebughini, S., Bracconi, M., Manelli, F., Lipp, S., Cuoci, A., & Maestri, M. (2015) · Chemical Engineering Journal · doi:10.1016/j.cej.2015.08.080 · Cited by 78 · see citing works

Modeling Morphology and Catalytic Activity of Nanoparticle Ensembles Under Reaction Conditions

Cheula, R., Maestri, M., & Mpourmpakis, G. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c01005 · Cited by 78 · see citing works

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Xu, J., Cao, X., & Hu, P. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d1cp01349a · Cited by 76 · see citing works

3D reconstruction and pore-scale modeling of coated catalytic filters for automotive exhaust gas aftertreatment

Kočí, P., Isoz, M., Plachá, M., et al. (2017) · Catalysis Today · doi:10.1016/j.cattod.2017.12.025 · Cited by 74 · see citing works

Evolution of Metastable Structures at Bimetallic Surfaces from Microscopy and Machine-Learning Molecular Dynamics

Lim, J. S., Vandermause, J., Spronsen, M. A. V., Musaelian, A., Xie, Y., Sun, L., O'Connor, C. R., Egle, T., Molinari, N., Florian, J., et al. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c06401 · Cited by 73 · see citing works

A cell agglomeration algorithm for accelerating detailed chemistry in CFD

Goldin, G. M., Ren, Z., & Zahirović, S. (2009) · Combustion Theory and Modelling · doi:10.1080/13647830903154542 · Cited by 71 · see citing works

Simulation at high temperature of atomic deposition, islands coalescence, Ostwald and inverse Ostwald ripening with a general simple kinetic Monte Carlo code

Lucas, S., & Moskovkin, P. (2010) · Thin Solid Films · doi:10.1016/j.tsf.2010.04.064 · Cited by 71 · see citing works

What happens in a catalytic fixed-bed reactor for n-butane oxidation to maleic anhydride? Insights from spatial profile measurements and particle resolved CFD simulations

Dong, Y., Geske, M., Korup, O., et al. (2018) · Chemical Engineering Journal · doi:10.1016/j.cej.2018.05.192 · Cited by 71 · see citing works

Unraveling the Role of the Rh–ZrO2 Interface in the Water–Gas-Shift Reaction via a First-Principles Microkinetic Study

Kauppinen, M. M., Melander, M., Bazhenov, A. S., et al. (2018) · ACS Catalysis · doi:10.1021/acscatal.8b02596 · Cited by 69 · see citing works

A generalized mathematical model to study gas transport in PEMFC porous media

Pant, L. M., Mitra, S. K., & Secanell, M. (2012) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2012.11.023 · Cited by 65 · see citing works

The use of dynamic adaptive chemistry and tabulation in reactive flow simulations

Ren, Z., Liu, Y., Lu, T., et al. (2013) · Combustion and Flame · doi:10.1016/j.combustflame.2013.08.018 · Cited by 64 · see citing works

Multiscale optimization using hybrid PDE/kMC process systems with application to thin film growth

Varshney, A. K., & Armaou, A. (2005) · Chemical Engineering Science · doi:10.1016/j.ces.2005.05.055 · Cited by 63 · see citing works

Enumerating Active Sites on Metal Nanoparticles: Understanding the Size Dependence of Cobalt Particles for CO Dissociation

van Etten, M. P. C., Zijlstra, B., Hensen, E. J. M., et al. (2021) · ACS Catalysis · doi:10.1021/acscatal.1c00651 · Cited by 63 · see citing works

Microkinetic modeling in electrocatalysis: Applications, limitations, and recommendations for reliable mechanistic insights

Baz, A., Dix, S. T., Holewinski, A., & Linic, S. (2021) · Journal of Catalysis · doi:10.1016/j.jcat.2021.08.043 · Cited by 62 · see citing works

A Fundamental Investigation of Gas/Solid Heat and Mass Transfer in Structured Catalysts Based on Periodic Open Cellular Structures (POCS)

Ferroni, C., Bracconi, M., Ambrosetti, M., Maestri, M., Groppi, G., & Tronconi, E. (2021) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.1c00215 · Cited by 61 · see citing works

Computer-Generated Kinetics for Coupled Heterogeneous/Homogeneous Systems: A Case Study in Catalytic Combustion of Methane on Platinum

Blöndal, K., Jelic, J., Mazeau, E., et al. (2019) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.9b01464 · Cited by 59 · see citing works

Random Forests for mapping and analysis of microkinetics models

Partopour, B., Paffenroth, R. C., & Dixon, A. G. (2018) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2018.04.019 · Cited by 57 · see citing works

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO 2 Hydrogenation on Ni(111)

Kreitz, B., Sargsyan, K., Blöndal, K., Mazeau, E., West, R. H., Wehinger, G. D., Turek, T., & Goldsmith, C. F. (2021) · JACS Au · doi:10.1021/jacsau.1c00276 · Cited by 57 · see citing works

Discerning complex reaction networks using automated generators

Vernuccio, S., & Broadbelt, L. J. (2019) · AIChE Journal · doi:10.1002/aic.16663 · Cited by 57 · see citing works

Multiscale modeling and control of Kappa number and porosity in a batch‐type pulp digester

Choi, H., & Kwon, J. S. (2019) · AIChE Journal · doi:10.1002/aic.16589 · Cited by 56 · see citing works

Escaping the trap of complication and complexity in multiscale microkinetic modelling of heterogeneous catalytic processes

Maestri, M. (2017) · Chemical Communications · doi:10.1039/c7cc05740g · Cited by 55 · see citing works

Structuration and Dynamics of Interfacial Liquid Water at Hydrated γ-Alumina Determined by ab Initio Molecular Simulations: Implications for Nanoparticle Stability

Réocreux, R., Jiang, T., Iannuzzi, M., Michel, C., & Sautet, P. (2017) · ACS Applied Nano Materials · doi:10.1021/acsanm.7b00100 · Cited by 54 · see citing works

Using microkinetic models to understand electrocatalytic reactions

Marshall, A. T. (2017) · Current Opinion in Electrochemistry · doi:10.1016/j.coelec.2017.10.024 · Cited by 53 · see citing works

First-principles microkinetics simulations of electrochemical reduction of CO2 over Cu catalysts

Zijlstra, B., Zhang, X., Liu, J., Filot, I. A. W., Zhou, Z., Sun, S., & Hensen, E. J. M. (2020) · Electrochimica Acta · doi:10.1016/j.electacta.2020.135665 · Cited by 52 · see citing works

Modeling the selective oxidation of n-butane to maleic anhydride: From active site to industrial reactor

Müller, M., Kutscherauer, M., Böcklein, S., et al. (2021) · Catalysis Today · doi:10.1016/j.cattod.2021.04.009 · Cited by 52 · see citing works

Mesoscale Elucidation of Solid Electrolyte Interphase Layer Formation in Li-Ion Battery Anode

Hao, F., Liu, Z., Balbuena, P. B., & Mukherjee, P. P. (2017) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.7b09465 · Cited by 50 · see citing works

Load-flexible fixed-bed reactors by multi-period design optimization

Zimmermann, R. T., Bremer, J., & Sundmacher, K. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.130771 · Cited by 50 · see citing works

A three dimensional kinetic Monte Carlo model for simulating the carbon/sulfur mesostructural evolutions of discharging lithium sulfur batteries

Thangavel, V., Guerrero-Gutiérrez, O. X., Quiroga, M., et al. (2019) · Energy storage materials · doi:10.1016/j.ensm.2019.07.011 · Cited by 49 · see citing works

Computational Fluid Dynamics Modeling of Radiation in a Steam Methane Reforming Fixed-Bed Reactor

Wehinger, G. D., & Flaischlen, S. (2019) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.9b01265 · Cited by 49 · see citing works

Multiscale Models for Light-Driven Processes

Nottoli, M., Cupellini, L., Lipparini, F., et al. (2021) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-090419-104031 · Cited by 49 · see citing works

Computational Studies of Interfacial Reactions at Anode Materials: Initial Stages of the Solid-Electrolyte-Interphase Layer Formation

Ramos‐Sánchez, G., Soto, F. A., de la Hoz, J. M. M., Liu, Z., Mukherjee, P. P., El‐Mellouhi, F., Seminario, J. M., & Balbuena, P. B. (2016) · Journal of Electrochemical Energy Conversion and Storage · doi:10.1115/1.4034412 · Cited by 48 · see citing works

Training set design for machine learning techniques applied to the approximation of computationally intensive first-principles kinetic models

Bracconi, M., & Maestri, M. (2020) · Chemical Engineering Journal · doi:10.1016/j.cej.2020.125469 · Cited by 48 · see citing works

Two-dimensional detailed modeling of fuel-rich H2 combustion over Rh/Al2O3 catalyst

Maestri, M., Beretta, A., Faravelli, T., et al. (2008) · Chemical Engineering Science · doi:10.1016/j.ces.2008.02.024 · Cited by 47 · see citing works

Intensification of load flexible fixed bed reactors by optimal design of staged reactor setups

Fischer, K. L., & Freund, H. (2020) · Chemical Engineering and Processing - Process Intensification · doi:10.1016/j.cep.2020.108183 · Cited by 46 · see citing works

Modeling Investigation of the Local Electrochemistry in Lithium-O2Batteries: A Kinetic Monte Carlo Approach

Blanquer, G., Yin, Y., Quiroga, M. A., et al. (2015) · Journal of The Electrochemical Society · doi:10.1149/2.0841602jes · Cited by 45 · see citing works

Systematic Enumeration of Elementary Reaction Steps in Surface Catalysis

Margraf, J. T., & Reuter, K. (2019) · ACS Omega · doi:10.1021/acsomega.8b03200 · Cited by 45 · see citing works

A robust nonlinear model predictive controller for a multiscale thin film deposition process

Rasoulian, S., & Ricardez‐Sandoval, L. (2015) · Chemical Engineering Science · doi:10.1016/j.ces.2015.02.002 · Cited by 44 · see citing works

Complexities in modeling of heterogeneous catalytic reactions

Keil, F. J. (2013) · Computers & Mathematics with Applications · doi:10.1016/j.camwa.2012.11.023 · Cited by 44 · see citing works

Modeling and control of cell wall thickness in batch delignification

Choi, H., & Kwon, J. S. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.06.025 · Cited by 44 · see citing works

Automated reaction database and reaction network analysis: extraction of reaction templates using cheminformatics

Plehiers, P. P., Marin, G. B., Stevens, C. V., et al. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0269-8 · Cited by 42 · see citing works

110th Anniversary : Commentary: CFD as a Modeling Tool for Fixed Bed Reactors

Partopour, B., & Dixon, A. G. (2019) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.8b06380 · Cited by 42 · see citing works

Grand Challenges in Computational Catalysis

Studt, F. (2021) · Frontiers in Catalysis · doi:10.3389/fctls.2021.658965 · Cited by 42 · see citing works

Ab initio Kinetic Monte Carlo simulations of dissolution at the NaCl–water interface

Chen, J., Reischl, B., Spijker, P., Holmberg, N., Laasonen, K. E., & Foster, A. S. (2014) · Physical Chemistry Chemical Physics · doi:10.1039/c4cp02375g · Cited by 41 · see citing works

<i>In situ</i> adaptive tabulation for the CFD simulation of heterogeneous reactors based on operator‐splitting algorithm

Bracconi, M., Maestri, M., & Cuoci, A. (2017) · AIChE Journal · doi:10.1002/aic.15441 · Cited by 41 · see citing works

Molecular Modelling for Reactor Design

Keil, F. J. (2018) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-060817-084141 · Cited by 41 · see citing works

Direct coupling of continuum and kinetic Monte Carlo models for multiscale simulation of electrochemical systems

Röder, F., Braatz, R. D., & Krewer, U. (2018) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2018.12.016 · Cited by 41 · see citing works

Multiscale Modeling in Chemical Vapor Deposition Processes: Models and Methodologies

Cheimarios, N., Kokkoris, G., & Boudouvis, A. G. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-019-09398-w · Cited by 41 · see citing works

Automated Mechanism Generation Using Linear Scaling Relationships and Sensitivity Analyses Applied to Catalytic Partial Oxidation of Methane

Mazeau, E., Satpute, P., Blöndal, K., et al. (2021) · ACS Catalysis · doi:10.1021/acscatal.0c04100 · Cited by 41 · see citing works

Engineering the Surface Architecture of Highly Dilute Alloys: An ab Initio Monte Carlo Approach

Papanikolaou, K. G., Darby, M. T., & Stamatakis, M. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04029 · Cited by 40 · see citing works

Porosity and Structure of Hierarchically Porous Ni/Al2O3 Catalysts for CO2 Methanation

Weber, S., Abel, K. L., Zimmermann, R. T., et al. (2020) · Catalysts · doi:10.3390/catal10121471 · Cited by 40 · see citing works

Dealloying of platinum-based alloy catalysts: Kinetic Monte Carlo simulations

Callejas-Tovar, R., Díaz, C., de la Hoz, J. M. M., & Balbuena, P. B. (2013) · Electrochimica Acta · doi:10.1016/j.electacta.2013.01.053 · Cited by 39 · see citing works

Multiscale Simulations for Lithium-Ion Batteries: SEI Film Growth and Capacity Fading

Shinagawa, C., Ushiyama, H., & Yamashita, K. (2017) · Journal of The Electrochemical Society · doi:10.1149/2.0381713jes · Cited by 39 · see citing works

Hierarchical Refinement of Microkinetic Models: Assessment of the Role of the WGS and r-WGS Pathways in CH 4 Partial Oxidation on Rh

Maestri, M., Livio, D., Beretta, A., et al. (2014) · Industrial & Engineering Chemistry Research · doi:10.1021/ie501570b · Cited by 38 · see citing works

Density functional theory models for electrocatalytic reactions

Maheshwari, S., Li, Y., Agrawal, N., & Janik, M. J. (2018) · Advances in catalysis · doi:10.1016/bs.acat.2018.10.003 · Cited by 38 · see citing works

Particle-Resolved Computational Fluid Dynamics as the Basis for Thermal Process Intensification of Fixed-Bed Reactors on Multiple Scales

Jurtz, N., Srivastava, U., Moghaddam, A. A., et al. (2021) · Energies · doi:10.3390/en14102913 · Cited by 38 · see citing works

An improved contact modification routine for a computationally efficient CFD simulation of packed beds

Kutscherauer, M., Böcklein, S., Mestl, G., et al. (2021) · Chemical Engineering Journal Advances · doi:10.1016/j.ceja.2021.100197 · Cited by 37 · see citing works

Prediction of morphological changes of catalyst materials under reaction conditions by combined ab initio thermodynamics and microkinetic modelling

Cheula, R., Soon, A., & Maestri, M. (2018) · Catalysis Science & Technology · doi:10.1039/c8cy00583d · Cited by 36 · see citing works

Modeling and Validating Fixed‐Bed Reactors: A State‐of‐the‐Art Review

Stegehake, C., Riese, J., & Grünewald, M. (2019) · ChemBioEng Reviews · doi:10.1002/cben.201900002 · Cited by 36 · see citing works

Model Based Multiscale Analysis of Film Formation in Lithium‐Ion Batteries

Röder, F., Laue, V., & Krewer, U. (2019) · Batteries & Supercaps · doi:10.1002/batt.201800107 · Cited by 35 · see citing works

Impact of washcoat distribution on the catalytic performance of gasoline particulate filters as predicted by lattice Boltzmann simulations

Belot, I., Vidal, D. J.-E., Greiner, R., et al. (2020) · Chemical Engineering Journal · doi:10.1016/j.cej.2020.127040 · Cited by 35 · see citing works

A computational approach to characterize formation of a passivation layer in lithium metal anodes

Sitapure, N., Lee, H., Ospina‐Acevedo, F., et al. (2020) · AIChE Journal · doi:10.1002/aic.17073 · Cited by 35 · see citing works

Active Site Representation in First-Principles Microkinetic Models: Data-Enhanced Computational Screening for Improved Methanation Catalysts

Deimel, M., Reuter, K., & Andersen, M. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c04045 · Cited by 35 · see citing works

Use of CFD for pressure drop, liquid saturation and wetting predictions in trickle bed reactors for different catalyst particle shapes

Bouras, H., Haroun, Y., Bodziony, F., Philippe, R., Fongarland, P., & Augier, F. (2021) · Chemical Engineering Science · doi:10.1016/j.ces.2021.117315 · Cited by 34 · see citing works

REDIM reduced modeling of quenching at a cold wall including heterogeneous wall reactions

Straßacker, C., Bykov, V., & Maas, U. (2018) · International Journal of Heat and Fluid Flow · doi:10.1016/j.ijheatfluidflow.2017.12.011 · Cited by 34 · see citing works

Probing local diffusion and reaction in a porous catalyst pellet

Sosna, B., Korup, O., & Horn, R. (2019) · Journal of Catalysis · doi:10.1016/j.jcat.2019.11.005 · Cited by 34 · see citing works

Spatiotemporal Investigation of the Temperature and Structure of a Pt/CeO2 Oxidation Catalyst for CO and Hydrocarbon Oxidation during Pulse Activation

Maurer, F., Gänzler, A. M., Lott, P., et al. (2021) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.0c05798 · Cited by 33 · see citing works

Coupling CFD–DEM and microkinetic modeling of surface chemistry for the simulation of catalytic fluidized systems

Uglietti, R., Bracconi, M., & Maestri, M. (2018) · Reaction Chemistry & Engineering · doi:10.1039/c8re00050f · Cited by 32 · see citing works

Characterization of heat transfer in consolidated, highly porous media using a hybrid-scale CFD approach

Meinicke, S., Dubil, K., Wetzel, T., et al. (2019) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2019.119201 · Cited by 32 · see citing works

Development and Assessment of a Criterion for the Application of Brønsted–Evans–Polanyi Relations for Dissociation Catalytic Reactions at Surfaces

Ding, Z., & Maestri, M. (2019) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.9b01628 · Cited by 32 · see citing works

Identifying the oxygen evolution mechanism by microkinetic modelling of cyclic voltammograms

Geppert, J., Kubannek, F., Röse, P., et al. (2021) · Electrochimica Acta · doi:10.1016/j.electacta.2021.137902 · Cited by 32 · see citing works

Microkinetic Modeling of the CO2 Desorption from Supported Multifaceted Ni Catalysts

Kreitz, B., Wehinger, G. D., Goldsmith, C. F., & Turek, T. (2021) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c09985 · Cited by 32 · see citing works

Enhancing the Thermal Performance of Slender Packed Beds through Internal Heat Fins

Jurtz, N., Flaischlen, S., Scherf, S. C., Kraume, M., & Wehinger, G. D. (2020) · Processes · doi:10.3390/pr8121528 · Cited by 31 · see citing works

Multiscale Modeling of Reaction and Diffusion in Zeolites: From the Molecular Level to the Reactor

Hansen, N., & Keil, F. J. (2011) · Soft Materials · doi:10.1080/1539445x.2011.599708 · Cited by 30 · see citing works

Nature‐Inspired, Computer‐Assisted Optimization of Hierarchically Structured Zeolites

Coppens, M., Weißenberger, T., Zhang, Q., & Ye, G. (2020) · Advanced Materials Interfaces · doi:10.1002/admi.202001409 · Cited by 30 · see citing works

Speeding up turbulent reactive flow simulation via a deep artificial neural network: A methodology study

Ouyang, Y., Vandewalle, L. A., Chen, L., Plehiers, P. P., Dobbelaere, M. R., Heynderickx, G. J., Marin, G. B., & Geem, K. M. V. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.132442 · Cited by 30 · see citing works

Computational Investigation of Aqueous Phase Effects on the Dehydrogenation and Dehydroxylation of Polyols over Pt(111)

Saleheen, M., Zare, M., Faheem, M., & Heyden, A. (2019) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.9b04994 · Cited by 30 · see citing works

Oxidative Coupling of Methane in Membrane Reactors; A Techno-Economic Assessment

Cruellas, A., Heezius, J., Spallina, V., et al. (2020) · Processes · doi:10.3390/pr8030274 · Cited by 30 · see citing works

Accelerating particle-resolved CFD simulations of catalytic fixed-bed reactors with DUO

Daymo, E. A., Hettel, M., Deutschmann, O., & Wehinger, G. D. (2022) · Chemical Engineering Science · doi:10.1016/j.ces.2021.117408 · Cited by 29 · see citing works

Multiscale Molecular Dynamics Simulation of Plasma Processing: Application to Plasma Sputtering

Brault, P. (2018) · Frontiers in Physics · doi:10.3389/fphy.2018.00059 · Cited by 29 · see citing works

Combustion kinetic model development using surrogate model similarity method

Wang, J., Li, S., & Yang, B. (2018) · Combustion Theory and Modelling · doi:10.1080/13647830.2018.1454607 · Cited by 28 · see citing works

Multi-scale reduced order models for transient simulation of multi-layered monolith reactors

Ratnakar, R. R., Dadi, R. K., & Balakotaiah, V. (2018) · Chemical Engineering Journal · doi:10.1016/j.cej.2018.04.053 · Cited by 28 · see citing works

Achievements and Expectations in the Field of Computational Heterogeneous Catalysis in an Innovation Context

Chizallet, C. (2021) · Topics in Catalysis · doi:10.1007/s11244-021-01489-y · Cited by 28 · see citing works

A hierarchical approach to chemical reactor engineering: an application to micro packed bed reactors

Rebughini, S., Bracconi, M., Dixon, A. G., & Maestri, M. (2018) · Reaction Chemistry & Engineering · doi:10.1039/c7re00195a · Cited by 27 · see citing works

Nature and identity of the active site via structure-dependent microkinetic modeling: An application to WGS and reverse WGS reactions on Rh

Cheula, R., & Maestri, M. (2021) · Catalysis Today · doi:10.1016/j.cattod.2021.05.016 · Cited by 27 · see citing works

In situ adaptive tabulation (ISAT) to accelerate transient computational fluid dynamics with complex heterogeneous chemical kinetics

Blasi, J. M., & Kee, R. J. (2016) · Computers &amp; Chemical Engineering · doi:10.1016/j.compchemeng.2015.08.020 · Cited by 26 · see citing works

Selection of equipment modules for a flexible modular production plant by a multi-objective evolutionary algorithm

Radatz, H., Schröder, M., Becker, C., et al. (2018) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2018.12.009 · Cited by 26 · see citing works

Particle dynamics in a multi-staged fluidized bed: Particle transport behavior on micro-scale by discrete particle modelling

Diez, E., Kieckhefen, P., Meyer, K., et al. (2019) · Advanced Powder Technology · doi:10.1016/j.apt.2019.05.025 · Cited by 26 · see citing works

Parameter optimization for the oxidative coupling of methane in a fixed bed reactor by combination of response surface methodology and computational fluid dynamics

Eppinger, T., Wehinger, G. D., & Kraume, M. (2013) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2013.12.017 · Cited by 25 · see citing works

catchyFOAM: Euler–Euler CFD Simulations of Fluidized Bed Reactors with Microkinetic Modeling of Gas-Phase and Catalytic Surface Chemistry

Vandewalle, L. A., Marin, G. B., & Geem, K. M. V. (2020) · Energy & Fuels · doi:10.1021/acs.energyfuels.0c02824 · Cited by 24 · see citing works

Coupling Euler–Euler and Microkinetic Modeling for the Simulation of Fluidized Bed Reactors: an Application to the Oxidative Coupling of Methane

Micale, D., Uglietti, R., Bracconi, M., & Maestri, M. (2021) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.0c05845 · Cited by 23 · see citing works

Reliable Molecular Dynamics: Uncertainty quantification using interval analysis in molecular dynamics simulation

Tran, A., & Wang, Y. (2016) · Computational Materials Science · doi:10.1016/j.commatsci.2016.10.021 · Cited by 23 · see citing works

Development and assessment of speed-up algorithms for the reactive CFD–DEM simulation of fluidized bed reactors

Uglietti, R., Bracconi, M., & Maestri, M. (2020) · Reaction Chemistry &amp; Engineering · doi:10.1039/c9re00440h · Cited by 22 · see citing works

Predicting catalyst-support interactions between metal nanoparticles and amorphous silica supports

Ewing, C. S., Veser, G., McCarthy, J. J., et al. (2016) · Surface Science · doi:10.1016/j.susc.2016.03.004 · Cited by 21 · see citing works

Product Design and Engineering in Chemical Engineering: Past, Present State, and Future

Uhlemann, J., Costa, R., & Charpentier, J. (2019) · Chemical Engineering & Technology · doi:10.1002/ceat.201900236 · Cited by 20 · see citing works

Demystifying the Atomistic Origin of the Electric Field Effect on Methane Oxidation

Panaritis, C., Hajar, Y. M., Treps, L., et al. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c01485 · Cited by 20 · see citing works

Machine learning based interpretation of microkinetic data: a Fischer–Tropsch synthesis case study

Chakkingal, A., Janssens, P., Poissonnier, J., et al. (2021) · Reaction Chemistry & Engineering · doi:10.1039/d1re00351h · Cited by 20 · see citing works

Cell agglomeration algorithm for coupling microkinetic modeling and steady-state CFD simulations of catalytic reactors

Rebughini, S., Cuoci, A., Dixon, A. G., & Maestri, M. (2017) · Computers &amp; Chemical Engineering · doi:10.1016/j.compchemeng.2016.11.033 · Cited by 19 · see citing works

Multiscale modelling, simulation and computing: from the desktop to the exascale

Hoekstra, A. G., Zwart, S. P., & Coveney, P. V. (2019) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2018.0355 · Cited by 19 · see citing works

Efficient Implementation of Cluster Expansion Models in Surface Kinetic Monte Carlo Simulations with Lateral Interactions: Subtraction Schemes, Supersites, and the Supercluster Contraction

Heß, F. (2019) · Journal of Computational Chemistry · doi:10.1002/jcc.26041 · Cited by 18 · see citing works

Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing

Bracconi, M. (2022) · Chemical Engineering and Processing - Process Intensification · doi:10.1016/j.cep.2022.109148 · Cited by 42 · see citing works

Efficient neural network models of chemical kinetics using a latent asinh rate transformation

Döppel, F. A., & Votsmeier, M. (2023) · Reaction Chemistry &amp; Engineering · doi:10.1039/d3re00212h · Cited by 24 · see citing works

Corrective Method for Status-Quo Bias by User’s Inputting Reason for Selection and Presenting Criticism Information

Shimizu, T., Futami, K., Terada, T., & Tsukamoto, M. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-40978-3_28

Change We Can Believe In

Johnson, I. R., & Fujita, K. (2012) · Psychological Science · doi:10.1177/0956797611423670 · Cited by 71 · see citing works

Limited attention and status quo bias

Dean, M., Kıbrıs, Ö., & Masatlıoĝlu, Y. (2017) · Journal of Economic Theory · doi:10.1016/j.jet.2017.01.009 · Cited by 130 · see citing works

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Qi, J. (2023) · Advances in Economics Management and Political Sciences · doi:10.54254/2754-1169/12/20230634 · Cited by 1 · see citing works

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