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  • Perspective
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Exploring catalytic reaction networks with machine learning

Nature Catalysis volume 6, pages 112–121 (2023)Cite this article

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Abstract

Chemical reaction networks form the heart of microkinetic models, which are one of the key tools available for gaining detailed mechanistic insight into heterogeneous catalytic processes. The exploration of complex chemical reaction networks is therefore a central task in current catalysis research. Unfortunately, microscopic experimental information about which elementary reaction steps are relevant to a given process is almost always sparse, making the inference of networks from experiments alone almost impossible. While computational approaches provide important complementary insights to this end, their predictions also come with substantial uncertainties related to the underlying approximations and, crucially, the use of idealized structure models. In this Perspective, we aim to shine a light on recent applications of machine learning in the context of catalytic reaction networks, aiding both the inference of effective kinetic rate laws from experiment and the computational exploration of chemical reaction networks.

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Fig. 1: Graph representation of a chemical reaction network.
Fig. 2: Use of surrogate models in optimization tasks pertinent to heterogeneous catalysis.
Fig. 3: Surrogate ML models for optimization tasks.
Fig. 4: Learning collective variables.
Fig. 5: Learning effective kinetics.

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References

  1. Pablo-García, S., García-Muelas, R., Sabadell-Rendón, A. & López, N. Dimensionality reduction of complex reaction networks in heterogeneous catalysis: from linear-scaling relationships to statistical learning techniques. Wiley Interdiscip. Rev. Comput. Mol. Sci. 11, e1540 (2021).

    Article  Google Scholar 

  2. Ulissi, Z. W., Medford, A. J., Bligaard, T. & Nørskov, J. K. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat. Commun. 8, 14621 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Stocker, S., Csányi, G., Reuter, K. & Margraf, J. T. Machine learning in chemical reaction space. Nat. Commun. 11, 5505 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Stamatakis, M. Kinetic modelling of heterogeneous catalytic systems. J. Phys. Condens. Matter 27, 013001 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Sutton, J. E. & Vlachos, D. G. Building large microkinetic models with first-principles’ accuracy at reduced computational cost. Chem. Eng. Sci. 121, 190–199 (2015).

    Article  CAS  Google Scholar 

  6. Reuter, K. Ab initio thermodynamics and first-principles microkinetics for surface catalysis. Catal. Lett. 146, 541–563 (2016).

    Article  CAS  Google Scholar 

  7. Bruix, A., Margraf, J. T., Andersen, M. & Reuter, K. First-principles-based multiscale modelling of heterogeneous catalysis. Nat. Catal. 2, 659–670 (2019).

    Article  CAS  Google Scholar 

  8. Kim, Y., Kim, J. W., Kim, Z. & Kim, W. Y. Efficient prediction of reaction paths through molecular graph and reaction network analysis. Chem. Sci. 9, 825–835 (2018).

    Article  CAS  PubMed  Google Scholar 

  9. Unsleber, J. P. & Reiher, M. The exploration of chemical reaction networks. Annu. Rev. Phys. Chem. 71, 121–142 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. Reuter, K., Plaisance, C. P., Oberhofer, H. & Andersen, M. Perspective: on the active site model in computational catalyst screening. J. Chem. Phys. 146, 040901 (2017).

    Article  PubMed  Google Scholar 

  11. Newton, M. A. Dynamic adsorbate/reaction induced structural change of supported metal nanoparticles: heterogeneous catalysis and beyond. Chem. Soc. Rev. 37, 2644–2657 (2008).

    Article  CAS  PubMed  Google Scholar 

  12. Reuter, K., Frenkel, D. & Scheffler, M. The steady state of heterogeneous catalysis, studied by first-principles statistical mechanics. Phys. Rev. Lett. 93, 116105 (2004).

    Article  PubMed  Google Scholar 

  13. Honkala, K. et al. Ammonia synthesis from first-principles calculations. Science 307, 555–558 (2005).

    Article  CAS  PubMed  Google Scholar 

  14. Mills, G., Jónsson, H. & Schenter, G. K. Reversible work transition state theory: application to dissociative adsorption of hydrogen. Surf. Sci. 324, 305–337 (1995).

    Article  CAS  Google Scholar 

  15. Margraf, J. T. & Reuter, K. Systematic enumeration of elementary reaction steps in surface catalysis. ACS Omega 4, 3370–3379 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bligaard, T. et al. The Brønsted–Evans–Polanyi relation and the volcano curve in heterogeneous catalysis. J. Catal. 224, 206–217 (2004).

    Article  CAS  Google Scholar 

  17. Wang, S. et al. Universal Brønsted–Evans–Polanyi relations for C–C, C–O, C–N, N–O, N–N, and O–O dissociation reactions. Catal. Lett. 141, 370–373 (2011).

    Article  CAS  Google Scholar 

  18. del Río, E. G., Mortensen, J. J. & Jacobsen, K. W. Local Bayesian optimizer for atomic structures. Phys. Rev. B 100, 104103 (2019).

    Article  Google Scholar 

  19. Denzel, A. & Kästner, J. Gaussian process regression for geometry optimization. J. Chem. Phys. 148, 094114 (2018).

    Article  Google Scholar 

  20. Schmitz, G. & Christiansen, O. Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation. J. Chem. Phys. 148, 241704 (2018).

    Article  PubMed  Google Scholar 

  21. Chanussot, L. et al. Open Catalyst 2020 (OC20) dataset and community challenges. ACS Catal. 11, 6059–6072 (2021).

    Article  CAS  Google Scholar 

  22. Tran, R. et al. The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysis. Preprint at https://arxiv.org/abs/2206.08917 (2022).

  23. Musielewicz, J., Wang, X., Tian, T. & Ulissi, Z. W. Finetuna: fine-tuning accelerated molecular simulations. Mach. Learn. Sci. Technol. 3, 03LT01 (2022).

  24. Andersen, M. & Reuter, K. Adsorption enthalpies for catalysis modeling through machine-learned descriptors. Acc. Chem. Res. 54, 2741–2749 (2021).

    Article  CAS  PubMed  Google Scholar 

  25. Xu, W., Reuter, K. & Andersen, M. Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation. Nat. Comput. Sci. 2, 443–450 (2022).

    Article  Google Scholar 

  26. Li, Z., Wang, S., Chin, W. S., Achenie, L. E. & Xin, H. High-throughput screening of bimetallic catalysts enabled by machine learning. J. Mater. Chem. A 5, 24131–24138 (2017).

    Article  CAS  Google Scholar 

  27. Tran, K. & Ulissi, Z. W. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat. Catal. 1, 696–703 (2018).

    Article  CAS  Google Scholar 

  28. García-Muelas, R. & López, N. Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals. Nat. Commun. 10, 1827 (2019).

    Article  Google Scholar 

  29. Esterhuizen, J. A., Goldsmith, B. R. & Linic, S. Theory-guided machine learning finds geometric structure–property relationships for chemisorption on subsurface alloys. Chem 6, 3100–3117 (2020).

    Article  CAS  Google Scholar 

  30. Mamun, O., Winther, K. T., Boes, J. R. & Bligaard, T. A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts. npj Comput. Mater. 6, 177 (2020).

    Article  CAS  Google Scholar 

  31. Fung, V., Hu, G., Ganesh, P. & Sumpter, B. G. Machine learned features from density of states for accurate adsorption energy prediction. Nat. Commun. 12, 88 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bisbo, M. K. & Hammer, B. Efficient global structure optimization with a machine-learned surrogate model. Phys. Rev. Lett. 124, 086102 (2020).

    Article  CAS  PubMed  Google Scholar 

  33. Deringer, V. L., Pickard, C. J. & Csányi, G. Data-driven learning of total and local energies in elemental boron. Phys. Rev. Lett. 120, 156001 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Yamashita, T. et al. Crystal structure prediction accelerated by Bayesian optimization. Phys. Rev. Mater. 2, 013803 (2018).

    Article  Google Scholar 

  35. Kang, P.-L., Shang, C. & Liu, Z.-P. Large-scale atomic simulation via machine learning potentials constructed by global potential energy surface exploration. Acc. Chem. Res. 53, 2119–2129 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. Jørgensen, M. S., Larsen, U. F., Jacobsen, K. W. & Hammer, B. Exploration versus exploitation in global atomistic structure optimization. J. Phys. Chem. A 122, 1504–1509 (2018).

    Article  PubMed  Google Scholar 

  37. Mortensen, H. L., Meldgaard, S. A., Bisbo, M. K., Christiansen, M.-P. V. & Hammer, B. Atomistic structure learning algorithm with surrogate energy model relaxation. Phys. Rev. B 102, 075427 (2020).

    Article  CAS  Google Scholar 

  38. Timmermann, J. et al. IrO2 surface complexions identified through machine learning and surface investigations. Phys. Rev. Lett. 125, 206101 (2020).

  39. Timmermann, J. et al. Data-efficient iterative training of Gaussian approximation potentials: application to surface structure determination of rutile IrO2 and RuO2. J. Chem. Phys. 155, 244107 (2021).

    Article  CAS  PubMed  Google Scholar 

  40. Kaappa, S., del Río, E. G. & Jacobsen, K. W. Global optimization of atomic structures with gradient-enhanced Gaussian process regression. Phys. Rev. B 103, 174114 (2021).

    Article  CAS  Google Scholar 

  41. Ma, S., Huang, S.-D., Fang, Y.-H. & Liu, Z.-P. TiH hydride formed on amorphous black titania: unprecedented active species for photocatalytic hydrogen evolution. ACS Catal. 8, 9711–9721 (2018).

    Article  CAS  Google Scholar 

  42. Todorović, M., Gutmann, M. U., Corander, J. & Rinke, P. Bayesian inference of atomistic structure in functional materials. npj Comput. Mater. 5, 35 (2019).

    Article  Google Scholar 

  43. Westermayr, J., Chaudhuri, S., Jeindl, A., Hofmann, O. & Maurer, R. Long-range dispersion-inclusive machine learning potentials for structure search and optimization of hybrid organic–inorganic interfaces. Digit. Discov. 1, 463–475 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Järvi, J., Todorović, M. & Rinke, P. Efficient modeling of organic adsorbates on oxygen-intercalated graphene on Ir(111). Phys. Rev. B 105, 195304 (2022).

    Article  Google Scholar 

  45. Jennings, P. C., Lysgaard, S., Hummelshøj, J. S., Vegge, T. & Bligaard, T. Genetic algorithms for computational materials discovery accelerated by machine learning. npj Comput. Mater. 5, 46 (2019).

    Article  Google Scholar 

  46. Jung, H., Sauerland, L., Stocker, S., Reuter, K. & Margraf, J. T. Machine-learning driven global optimization of surface adsorbate geometries. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv-2022-q3j0s-v2 (2022).

  47. Peterson, A. A. Acceleration of saddle-point searches with machine learning. J. Chem. Phys. 145, 074106 (2016).

    Article  PubMed  Google Scholar 

  48. Koistinen, O.-P., Dagbjartsdóttir, F. B., Ásgeirsson, V., Vehtari, A. & Jónsson, H. Nudged elastic band calculations accelerated with Gaussian process regression. J. Chem. Phys. 147, 152720 (2017).

    Article  PubMed  Google Scholar 

  49. Denzel, A. & Kastner, J. Gaussian process regression for transition state search. J. Chem. Theory Comput. 14, 5777–5786 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Torres, J. A. G., Jennings, P. C., Hansen, M. H., Boes, J. R. & Bligaard, T. Low-scaling algorithm for nudged elastic band calculations using a surrogate machine learning model. Phys. Rev. Lett. 122, 156001 (2019).

    Article  Google Scholar 

  51. Henkelman, G. & Jónsson, H. A dimer method for finding saddle points on high dimensional potential surfaces using only first derivatives. J. Chem. Phys. 111, 7010–7022 (1999).

    Article  CAS  Google Scholar 

  52. Koistinen, O.-P., Ásgeirsson, V., Vehtari, A. & Jónsson, H. Minimum mode saddle point searches using Gaussian process regression with inverse-distance covariance function. J. Chem. Theory Comput. 16, 499–509 (2020).

    Article  PubMed  Google Scholar 

  53. Kang, P.-L., Shi, Y.-F., Shang, C. & Liu, Z.-P. Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivity. Chem. Sci. 13, 8148–8160 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Shi, Y.-F., Kang, P.-L., Shang, C. & Liu, Z.-P. Methanol synthesis from CO2/CO mixture on Cu–Zn catalysts from microkinetics-guided machine learning pathway search. J. Am. Chem. Soc. 144, 13401–13414 (2022).

    Article  CAS  PubMed  Google Scholar 

  55. Zhang, X.-J., Shang, C. & Liu, Z.-P. Stochastic surface walking reaction sampling for resolving heterogeneous catalytic reaction network: a revisit to the mechanism of water-gas shift reaction on Cu. J. Chem. Phys. 147, 152706 (2017).

    Article  PubMed  Google Scholar 

  56. Sugita, Y. & Okamoto, Y. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314, 141–151 (1999).

    Article  CAS  Google Scholar 

  57. Laio, A. & Parrinello, M. Escaping free-energy minima. Proc. Natl Acad. Sci. USA 99, 12562–12566 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Foppa, L., Iannuzzi, M., Copéret, C. & Comas-Vives, A. Adlayer dynamics drives CO activation in Ru-catalyzed Fischer–Tropsch synthesis. ACS Catal. 8, 6983–6992 (2018).

    Article  CAS  Google Scholar 

  59. Xu, J., Cao, X.-M. & Hu, P. Accelerating metadynamics-based free-energy calculations with adaptive machine learning potentials. J. Chem. Theory Comput. 17, 4465–4476 (2021).

    Article  CAS  PubMed  Google Scholar 

  60. Goldsmith, C. F. & West, R. H. Automatic generation of microkinetic mechanisms for heterogeneous catalysis. J. Phys. Chem. C 121, 9970–9981 (2017).

    Article  CAS  Google Scholar 

  61. Jafari, M. & Zimmerman, P. M. Uncovering reaction sequences on surfaces through graphical methods. Phys. Chem. Chem. Phys. 20, 7721–7729 (2018).

    Article  CAS  PubMed  Google Scholar 

  62. Iwasa, T. et al. Combined automated reaction pathway searches and sparse modeling analysis for catalytic properties of lowest energy twins of Cu13. J. Phys. Chem. A 123, 210–217 (2019).

    Article  CAS  PubMed  Google Scholar 

  63. Zhao, Q., Xu, Y., Greeley, J. & Savoie, B. M. Deep reaction network exploration at a heterogeneous catalytic interface. Nat. Commun. 13, 1219 (2022).

    Google Scholar 

  64. Bilodeau, C., Jin, W., Jaakkola, T., Barzilay, R. & Jensen, K. F. Generative models for molecular discovery: recent advances and challenges. Wiley Interdiscip. Rev. Comput. Mol. Sci. 12, e1608 (2022).

    Article  Google Scholar 

  65. Türk, H., Landini, E., Kunkel, C., Margraf, J. T. & Reuter, K. Assessing deep generative models in chemical composition space. Chem. Mater. 34, 9455–9467 (2022).

  66. Kim, S., Noh, J., Gu, G. H., Aspuru-Guzik, A. & Jung, Y. Generative adversarial networks for crystal structure prediction. ACS Cent. Sci. 6, 1412–1420 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Noé, F., Olsson, S., Köhler, J. & Wu, H. Boltzmann generators: sampling equilibrium states of many-body systems with deep learning. Science 365, eaaw1147 (2019).

    Article  PubMed  Google Scholar 

  68. Yoon, J. et al. Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy. Mach. Learn. Sci. Technol. 2, 045018 (2021).

    Article  Google Scholar 

  69. Sultan, M. M. & Pande, V. S. Automated design of collective variables using supervised machine learning. J. Chem. Phys. 149, 094106 (2018).

    Article  PubMed  Google Scholar 

  70. Rogal, J., Schneider, E. & Tuckerman, M. E. Neural-network-based path collective variables for enhanced sampling of phase transformations. Phys. Rev. Lett. 123, 245701 (2019).

    Article  CAS  PubMed  Google Scholar 

  71. Sun, L. et al. Multitask machine learning of collective variables for enhanced sampling of rare events. J. Chem. Theory Comput. 18, 2341–2353 (2022).

    Article  CAS  PubMed  Google Scholar 

  72. Simm, G. N. & Reiher, M. Error-controlled exploration of chemical reaction networks with Gaussian processes. J. Chem. Theory Comput. 14, 5238–5248 (2018).

    Article  CAS  PubMed  Google Scholar 

  73. Tran, K. et al. Methods for comparing uncertainty quantifications for material property predictions. Mach. Learn. Sci. Technol. 1, 025006 (2020).

    Article  Google Scholar 

  74. Winther, K. T. et al. Catalysis-Hub.org, an open electronic structure database for surface reactions. Sci. Data 6, 75 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Blackmond, D. G. Kinetic profiling of catalytic organic reactions as a mechanistic tool. J. Am. Chem. Soc. 137, 10852–10866 (2015).

    Article  CAS  PubMed  Google Scholar 

  76. Trunschke, A. Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective. Catal. Sci. Technol. 12, 3650–3669 (2022).

    Article  CAS  Google Scholar 

  77. Wehinger, G. D. et al. Quo vadis multiscale modeling in reaction engineering?—A perspective. Chem. Eng. Res. Des. 184, 39–58 (2022).

    Article  CAS  Google Scholar 

  78. Blackmond, D. G. Reaction progress kinetic analysis: a powerful methodology for mechanistic studies of complex catalytic reactions. Angew. Chem. Int. Ed. 44, 4302–4320 (2005).

    Article  CAS  Google Scholar 

  79. Burés, J. Variable time normalization analysis: general graphical elucidation of reaction orders from concentration profiles. Angew. Chem. Int. Ed. 55, 16084–16087 (2016).

    Article  Google Scholar 

  80. Nielsen, C. D.-T. & Burés, J. Visual kinetic analysis. Chem. Sci. 10, 348–353 (2019).

    Article  CAS  PubMed  Google Scholar 

  81. Burés, J. What is the order of a reaction? Top. Catal. 60, 631–633 (2017).

    Article  Google Scholar 

  82. Box, G. E. P. & Draper, N. R. Response Surfaces, Mixtures, and Ridge Analyses (Wiley, 2007).

    Book  Google Scholar 

  83. Felsen, F., Reuter, K. & Scheurer, C. A model-free sparse approximation approach to robust formal reaction kinetics. Chem. Eng. J. 433, 134121 (2022).

    Article  CAS  Google Scholar 

  84. Trunschke, A. et al. Towards experimental handbooks in catalysis. Top. Catal. 63, 1683–1699 (2020).

    Article  CAS  Google Scholar 

  85. Temel, B., Meskine, H., Reuter, K., Scheffler, M. & Metiu, H. Does phenomenological kinetics provide an adequate description of heterogeneous catalytic reactions? J. Chem. Phys. 126, 204711 (2007).

    Article  PubMed  Google Scholar 

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Acknowledgements

H.J. gratefully acknowledges support from the Alexander-von-Humboldt (AvH) Foundation.

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  1. Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany

    Johannes T. Margraf, Hyunwook Jung, Christoph Scheurer & Karsten Reuter

  2. Institute of Energy and Climate Research (IEK-9), Forschungszentrum Jülich, Jülich, Germany

    Christoph Scheurer

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Margraf, J.T., Jung, H., Scheurer, C. et al. Exploring catalytic reaction networks with machine learning. Nat Catal 6, 112–121 (2023). https://doi.org/10.1038/s41929-022-00896-y

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