Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Chemical reaction networks and opportunities for machine learning

Nature Computational Science volume 3, pages 12–24 (2023)Cite this article

Subjects

Abstract

Chemical reaction networks (CRNs), defined by sets of species and possible reactions between them, are widely used to interrogate chemical systems. To capture increasingly complex phenomena, CRNs can be leveraged alongside data-driven methods and machine learning (ML). In this Perspective, we assess the diverse strategies available for CRN construction and analysis in pursuit of a wide range of scientific goals, discuss ML techniques currently being applied to CRNs and outline future CRN-ML approaches, presenting scientific and technical challenges to overcome.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Diverse examples of CRNs.
Fig. 2: Construction and characterization of CRNs.
Fig. 3: Applications of ML to the construction and analysis of CRNs.

Similar content being viewed by others

References

  1. Manion, J. A., Sheen, D. A. & Awan, I. A. Evaluated kinetics of the reactions of H and CH3 with n-alkanes: experiments with n-butane and a combustion model reaction network analysis. J. Phys. Chem. A 119, 7637–7658 (2015).

    Article  Google Scholar 

  2. Gao, C. W., Allen, J. W., Green, W. H. & West, R. H. Reaction mechanism generator: automatic construction of chemical kinetic mechanisms. Comput. Phys. Commun. 203, 212–225 (2016).

    Article  Google Scholar 

  3. 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  Google Scholar 

  4. 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  Google Scholar 

  5. Steiner, M. & Reiher, M. Autonomous reaction network exploration in homogeneous and heterogeneous catalysis. Top. Catal. 65, 6–39 (2022).

    Article  Google Scholar 

  6. Blau, S. M. et al. A chemically consistent graph architecture for massive reaction networks applied to solid-electrolyte interphase formation. Chem. Sci. 12, 4931–4939 (2021).

    Article  Google Scholar 

  7. Xie, X. et al. Data-driven prediction of formation mechanisms of lithium ethylene monocarbonate with an automated reaction network. J. Am. Chem. Soc. 143, 13245–13258 (2021).

    Article  Google Scholar 

  8. Centler, F. & Dittrich, P. Chemical organizations in atmospheric photochemistries—a new method to analyze chemical reaction networks. Planet. Space Sci. 55, 413–428 (2007).

    Article  Google Scholar 

  9. Heald, C. L. & Kroll, J. H. The fuel of atmospheric chemistry: toward a complete description of reactive organic carbon. Sci. Adv. 6, eaay8967 (2020).

    Article  Google Scholar 

  10. Zhang, J.-T., Wang, H.-Y., Zhang, X., Zhang, F. & Guo, Y.-L. Study of short-lived and early reaction intermediates in organocatalytic asymmetric amination reactions by ion-mobility mass spectrometry. Catal. Sci. Technol. 6, 6637–6643 (2016).

    Article  Google Scholar 

  11. Williams, P. J. H. et al. New approach to the detection of short-lived radical intermediates. J. Am. Chem. Soc. 144, 15969–15976 (2022).

    Article  Google Scholar 

  12. Tyson, J. J. & Novák, B. Functional motifs in biochemical reaction networks. Annu. Rev. Phys. Chem. 61, 219–240 (2010).

    Article  Google Scholar 

  13. Wong, A. S. Y. & Huck, W. T. S. Grip on complexity in chemical reaction networks. Beilstein J. Org. Chem. 13, 1486–1497 (2017).

    Article  Google Scholar 

  14. Kowalik, M. et al. Parallel optimization of synthetic pathways within the network of organic chemistry. Angew. Chem. Int. Ed. 51, 7928–7932 (2012).

    Article  Google Scholar 

  15. Todd, P. K. et al. Selectivity in yttrium manganese oxide synthesis via local chemical potentials in hyperdimensional phase space. J. Am. Chem. Soc. 143, 15185–15194 (2021).

    Article  Google Scholar 

  16. Mikulak-Klucznik, B. et al. Computational planning of the synthesis of complex natural products. Nature 588, 83–88 (2020).

    Article  Google Scholar 

  17. Wołos, A. et al. Computer-designed repurposing of chemical wastes into drugs. Nature 604, 668–676 (2022).

    Article  Google Scholar 

  18. McDermott, M. J., Dwaraknath, S. S. & Persson, K. A. A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis. Nat. Commun. 12, 3097 (2021).

    Article  Google Scholar 

  19. Aykol, M., Montoya, J. H. & Hummelshøj, J. Rational solid-state synthesis routes for inorganic materials. J. Am. Chem. Soc. 143, 9244–9259 (2021).

    Article  Google Scholar 

  20. Feinberg, M. Foundations of Chemical Reaction Network Theory (Springer, 2019); https://doi.org/10.1007/978-3-030-03858-8

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

    Article  Google Scholar 

  22. Meuwly, M. Machine learning for chemical reactions. Chem. Rev. 121, 10218–10239 (2021).

    Article  Google Scholar 

  23. Garza, A. J., Bell, A. T. & Head-Gordon, M. Mechanism of CO2 reduction at copper surfaces: pathways to C2 products. ACS Catal. 8, 1490–1499 (2018).

    Article  Google Scholar 

  24. Lees, E. W., Bui, J. C., Song, D., Weber, A. Z. & Berlinguette, C. P. Continuum model to define the chemistry and mass transfer in a bicarbonate electrolyzer. ACS Energy Lett. 7, 834–842 (2022).

    Article  Google Scholar 

  25. Maeda, S., Harabuchi, Y., Takagi, M., Taketsugu, T. & Morokuma, K. Artificial force induced reaction (AFIR) method for exploring quantum chemical potential energy surfaces. Chem. Rec. 16, 2232–2248 (2016).

    Article  Google Scholar 

  26. Dewyer, A. L., Argüelles, A. J. & Zimmerman, P. M. Methods for exploring reaction space in molecular systems. WIREs Comput. Mol. Sci. 8, 1354 (2018).

  27. Simm, G. N., Vaucher, A. C. & Reiher, M. Exploration of reaction pathways and chemical transformation networks. J. Phys. Chem. A 123, 385–399 (2019).

    Article  Google Scholar 

  28. Zhao, Q. & Savoie, B. M. Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks. Nat. Comput. Sci. 1, 479–490 (2021).

    Article  Google Scholar 

  29. Zhao, Q., Xu, Y., Greeley, J. & Savoie, B. M. Deep reaction network exploration at a heterogeneous catalytic interface. Nat. Commun. 13, 4860 (2022).

    Article  Google Scholar 

  30. Blau, S., Spotte-Smith, E. W. C., Wood, B., Dwaraknath, S. & Persson, K. Accurate, automated density functional theory for complex molecules using on-the-fly error correction. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv.13076030.v1 (2020).

  31. Gothard, C. M. et al. Rewiring chemistry: algorithmic discovery and experimental validation of one-pot reactions in the network of organic chemistry. Angew. Chem. Int. Ed. 51, 7922–7927 (2012).

    Article  Google Scholar 

  32. Szymkuć, S. et al. Computer-assisted synthetic planning: the end of the beginning. Angew. Chem. Int. Ed. 55, 5904–5937 (2016).

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Liu, M. et al. Reaction mechanism generator v3.0: advances in automatic mechanism generation. J. Chem. Inf. Model. 61, 2686–2696 (2021).

    Article  Google Scholar 

  35. Rappoport, D., Galvin, C. J., Zubarev, D. Y. & Aspuru-Guzik, A. Complex chemical reaction networks from heuristics-aided quantum chemistry. J. Chem. Theory Comput. 10, 897–907 (2014).

    Article  Google Scholar 

  36. Wolos, A. et al. Synthetic connectivity, emergence, and self-regeneration in the network of prebiotic chemistry. Science 369, eaaw1955 (2020).

    Article  Google Scholar 

  37. Liao, K. H. et al. Application of biologically based computer modeling to simple or complex mixtures. Environ. Health Persp. 110, 957–963 (2002).

    Article  Google Scholar 

  38. Wicker, J., Fenner, K., Ellis, L., Wackett, L. & Kramer, S. Predicting biodegradation products and pathways: a hybrid knowledge-and machine learning-based approach. Bioinformatics 26, 814–821 (2010).

    Article  Google Scholar 

  39. Barter, D. et al. Predictive stochastic analysis of massive filter-based electrochemical reaction networks. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv-2021-c2gp3-v2 (2022).

  40. Segler, M. H., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018).

    Article  Google Scholar 

  41. Grzybowski, B.A., Badowski, T., Molga, K., Szymkuć, S.: Network search algorithms and scoring functions for advanced-level computerized synthesis planning. WIREs Comput. Mol. Sci. https://doi.org/10.1002/wcms.1630 (2022).

  42. Granda, J. M., Donina, L., Dragone, V., Long, D.-L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559, 377–381 (2018).

    Article  Google Scholar 

  43. Seifrid, M. et al. Autonomous chemical experiments: Challenges and perspectives on establishing a self-driving lab. Acc. Chem. Res. 55, 2454–2466 (2022).

    Article  Google Scholar 

  44. Benson, S. W. et al. Additivity rules for the estimation of thermochemical properties. Chem. Rev. 69, 279–324 (1969).

    Article  Google Scholar 

  45. Bell, R. P. & Hinshelwood, C. N. The theory of reactions involving proton transfers. Proc. R. Soc. Lond. A 154, 414–429 (1936).

    Article  Google Scholar 

  46. Evans, M. & Polanyi, M. Further considerations on the thermodynamics of chemical equilibria and reaction rates. Trans. Faraday Soc. 32, 1333–1360 (1936).

    Article  Google Scholar 

  47. Hatzimanikatis, V. et al. Exploring the diversity of complex metabolic networks. Bioinformatics 21, 1603–1609 (2005).

    Article  Google Scholar 

  48. Yu, J., Sumathi, R. & Green, W. H. Accurate and efficient method for predicting thermochemistry of polycyclic aromatic hydrocarbons—bond-centered group additivity. J. Am. Chem. Soc. 126, 12685–12700 (2004).

    Article  Google Scholar 

  49. Meng, Q. et al. A theoretical investigation on Bell–Evans–Polanyi correlations for hydrogen abstraction reactions of large biodiesel molecules by H and OH radicals. Combust. Flame 214, 394–406 (2020).

    Article  Google Scholar 

  50. Vijay, S., Kastlunger, G., Chan, K. & Nørskov, J. K. Limits to scaling relations between adsorption energies? J. Chem. Phys. 156, 231102 (2022).

    Article  Google Scholar 

  51. Mardirossian, N. & Head-Gordon, M. Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals. Mol. Phys. 115, 2315–2372 (2017).

    Article  Google Scholar 

  52. Jain, A. et al. Commentary: The Materials Project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).

    Article  Google Scholar 

  53. Schlögl, R. Heterogeneous catalysis. Angew. Chem. Int. Ed. 54, 3465–3520 (2015).

    Article  Google Scholar 

  54. Wei, Z., Li, Y., Cooks, R.G., Yan, X.: Accelerated reaction kinetics in microdroplets: Overview and recent developments. Annu. Rev. Phys. Chem. 71, 31–51 (2020).

  55. Heitele, H. Dynamic solvent effects on electron-transfer reactions. Angew. Chem. Int. Ed. 32, 359–377 (1993).

    Article  Google Scholar 

  56. Cativiela, C., Garcia, J., Mayoral, J. & Salvatella, L. Modelling of solvent effects on the Diels–Alder reaction. Chem. Soc. Rev. 25, 209–218 (1996).

    Article  Google Scholar 

  57. Murzin, D. Y. Solvent effects in catalysis: implementation for modelling of kinetics. Catal. Sci. Technol. 6, 5700–5713 (2016).

    Article  Google Scholar 

  58. Eigen, M. Proton transfer, acid-base catalysis, and enzymatic hydrolysis. part I: elementary processes. Angew. Chem. Int. Ed. 3, 1–19 (1964).

    Article  Google Scholar 

  59. Cordes, E. & Bull, H. Mechanism and catalysis for hydrolysis of acetals, ketals, and ortho esters. Chem. Rev. 74, 581–603 (1974).

    Article  Google Scholar 

  60. Schwaller, P. et al. Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy. Chem. Sci. 11, 3316–3325 (2020).

    Article  Google Scholar 

  61. Robertson, C., Ismail, I. & Habershon, S. Traversing dense networks of elementary chemical reactions to predict minimum-energy reaction mechanisms. ChemSystemsChem 2, 1900047 (2020).

    Article  Google Scholar 

  62. Dijkstra, E. W. et al. A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959).

    Article  MATH  Google Scholar 

  63. Yen, J. Y. An algorithm for finding shortest routes from all source nodes to a given destination in general networks. Quart. Appl. Math. 27, 526–530 (1970).

    Article  MATH  Google Scholar 

  64. Browne, C. B. et al. A survey of Monte Carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4, 1–43 (2012).

    Article  Google Scholar 

  65. Wang, X. et al. Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning. Chem. Sci. 11, 10959–10972 (2020).

    Article  Google Scholar 

  66. Lee, K., Woo Kim, J. & Youn Kim, W. Efficient construction of a chemical reaction network guided by a monte carlo tree search. ChemSystemsChem 2, 1900057 (2020).

    Article  Google Scholar 

  67. Gillespie, D. T. Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem. 58, 35–55 (2007).

    Article  Google Scholar 

  68. Barabási, A.-L. Scale-free networks: A decade and beyond. Science 325, 412–413 (2009).

    Article  MATH  Google Scholar 

  69. Grzybowski, B. A., Bishop, K. J. M., Kowalczyk, B. & Wilmer, C. E. The ‘wired’ universe of organic chemistry. Nat. Chem. 1, 31–36 (2009).

    Article  Google Scholar 

  70. Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabási, A.-L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000).

    Article  Google Scholar 

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

  72. Bishop, K. J. M., Klajn, R. & Grzybowski, B. A. The core and most useful molecules in organic chemistry. Angew. Chem. Int. Ed. 45, 5348–5354 (2006).

    Article  Google Scholar 

  73. Marshall, A. T. Using microkinetic models to understand electrocatalytic reactions. Curr. Opin. Electrochem. 7, 75–80 (2018).

    Article  Google Scholar 

  74. Vermeire, F. H. et al. Detailed kinetic modeling for the pyrolysis of a jet a surrogate. Energy Fuels 36, 1304–1315 (2022).

    Article  Google Scholar 

  75. Spotte-Smith, E. W. C. et al. Toward a mechanistic model of solid-electrolyte interphase formation and evolution in lithium-ion batteries. ACS Energy Lett. 7, 1446–1453 (2022).

    Article  Google Scholar 

  76. Zhang, H., Linford, J. C., Sandu, A. & Sander, R. Chemical mechanism solvers in air quality models. Atmosphere 2, 510–532 (2011).

    Article  Google Scholar 

  77. Miller, J. A. & Klippenstein, S. J. Master equation methods in gas phase chemical kinetics. J. Phys. Chem. A 110, 10528–10544 (2006).

    Article  Google Scholar 

  78. Gillespie, D. T. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340–2361 (1977).

    Article  Google Scholar 

  79. Byrne, G. D. & Hindmarsh, A. C. Stiff ODE solvers: a review of current and coming attractions. J. Comput. Phys. 70, 1–62 (1987).

    Article  MATH  Google Scholar 

  80. Klippenstein, S. J. From theoretical reaction dynamics to chemical modeling of combustion. Proc. Combust. Inst. 36, 77–111 (2017).

    Article  Google Scholar 

  81. Matera, S., Schneider, W. F., Heyden, A. & Savara, A. Progress in accurate chemical kinetic modeling, simulations, and parameter estimation for heterogeneous catalysis. ACS Catal. 9, 6624–6647 (2019).

    Article  Google Scholar 

  82. Thompson, A. P., Swiler, L. P., Trott, C. R., Foiles, S. M. & Tucker, G. J. Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials. J. Comput. Phys. 285, 316–330 (2015).

    Article  MATH  Google Scholar 

  83. Drautz, R. Atomic cluster expansion for accurate and transferable interatomic potentials. Phys. Rev. B 99, 014104 (2019).

    Article  Google Scholar 

  84. Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).

    Article  Google Scholar 

  85. Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).

    Article  Google Scholar 

  86. Smith, J. S., Isayev, O. & Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 8, 3192–3203 (2017).

    Article  Google Scholar 

  87. Zhang, L., Han, J., Wang, H., Car, R. & Weinan, E. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett. 120, 143001 (2018).

    Article  Google Scholar 

  88. Schütt, K. et al. SchNet: a continuous-filter convolutional neural network for modeling quantum interactions. Adv. Neural Inf. Process. Syst. 30, 992–1002 (2017).

  89. Shui, Z. et al. Injecting domain knowledge from empirical interatomic potentials to neural networks for predicting material properties. Preprint at https://arxiv.org/abs/2210.08047 (2022).

  90. Unke, O. T. et al. Machine learning force fields. Chem. Rev. 121, 10142–10186 (2021).

    Article  Google Scholar 

  91. Behler, J. Four generations of high-dimensional neural network potentials. Chem. Rev. 121, 10037–10072 (2021).

    Article  Google Scholar 

  92. Zubatyuk, R., Smith, J. S., Leszczynski, J. & Isayev, O. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network. Sci. Adv. 5, 6490 (2019).

    Article  Google Scholar 

  93. Spicher, S. & Grimme, S. Robust atomistic modeling of materials, organometallic, and biochemical systems. Angew. Chem. Int. Ed. 59, 15665–15673 (2020).

    Article  Google Scholar 

  94. Xie, X., Persson, K. A. & Small, D. W. Incorporating electronic information into machine learning potential energy surfaces via approaching the ground-state electronic energy as a function of atom-based electronic populations. J. Chem. Theory Comput. 16, 4256–4270 (2020).

    Article  Google Scholar 

  95. Ko, T. W., Finkler, J. A., Goedecker, S. & Behler, J. A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer. Nat. Commun. 12, 398 (2021).

    Article  Google Scholar 

  96. Wen, M. & Tadmor, E. B. Hybrid neural network potential for multilayer graphene. Phys. Rev. B 100, 195419 (2019).

    Article  Google Scholar 

  97. Novikov, I., Grabowski, B., Körmann, F. & Shapeev, A. Magnetic moment tensor potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe. npj Comput. Mater. 8, 13 (2022).

  98. Zeng, J., Cao, L., Xu, M., Zhu, T. & Zhang, J. Z. Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation. Nat. Commun. 11, 5713 (2020).

  99. 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 

  100. 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 

  101. Williams, C. K. & Rasmussen, C. E. Gaussian Processes for Machine Learning (MIT Press, 2006).

  102. Wen, M. & Tadmor, E. B. Uncertainty quantification in molecular simulations with dropout neural network potentials. npj Comput. Mater. 6, 124 (2020).

    Article  Google Scholar 

  103. Jackson, R., Zhang, W. & Pearson, J. TSNet: predicting transition state structures with tensor field networks and transfer learning. Chem. Sci. 12, 10022–10040 (2021).

    Article  Google Scholar 

  104. Spiekermann, K. A., Pattanaik, L. & Green, W. H. Fast predictions of reaction barrier heights: toward coupled-cluster accuracy. J. Phys. Chem. A 126, 3976–3986 (2022).

    Article  Google Scholar 

  105. Al Ibrahim, E. & Farooq, A. Transfer learning approach to multitarget temperature-dependent reaction rate prediction. J. Phys. Chem. A 126, 4617–4629 (2022).

    Article  Google Scholar 

  106. Wen, M., Blau, S. M., Xie, X., Dwaraknath, S. & Persson, K. Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining. Chem. Sci. 13, 1446–1458 (2022).

    Article  Google Scholar 

  107. Coley, C. W., Green, W. H. & Jensen, K. F. Machine learning in computer-aided synthesis planning. Acc. Chem. Res. 51, 1281–1289 (2018).

    Article  Google Scholar 

  108. Badowski, T., Gajewska, E. P., Molga, K. & Grzybowski, B. A. Synergy between expert and machine-learning approaches allows for improved retrosynthetic planning. Angew. Chem. Int. Ed. 59, 725–730 (2020).

    Article  Google Scholar 

  109. Lan, T. & An, Q. Discovering catalytic reaction networks using deep reinforcement learning from first-principles. J. Am. Chem. Soc. 143, 16804–16812 (2021).

    Article  Google Scholar 

  110. Wang, Q., Mao, Z., Wang, B. & Guo, L. Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowledge Data Eng. 29, 2724–2743 (2017).

    Article  Google Scholar 

  111. Ji, S., Pan, S., Cambria, E., Marttinen, P. & Philip, S. Y. A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33, 494–514 (2022).

    Article  Google Scholar 

  112. Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 6000–6010 (2017).

    Google Scholar 

  113. Brown, T. et al. Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33, 1877–1901 (2020).

  114. Trewartha, A. et al. Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science. Patterns 3, 100488 (2022).

    Article  Google Scholar 

  115. Kononova, O. et al. Text-mined dataset of inorganic materials synthesis recipes. Sci. Data 6, 203 (2019).

    Article  Google Scholar 

  116. Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571, 95–98 (2019).

    Article  Google Scholar 

  117. Mehr, S. H. M., Craven, M., Leonov, A. I., Keenan, G. & Cronin, L. A universal system for digitization and automatic execution of the chemical synthesis literature. Science 370, 101–108 (2020).

    Article  Google Scholar 

  118. Steiner, S. et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science 363, aav2211 (2019).

  119. Häse, F., Roch, L. M. & Aspuru-Guzik, A. Next-generation experimentation with self-driving laboratories. Trends Chem. 1, 282–291 (2019).

    Article  Google Scholar 

  120. Rohrbach, S. et al. Digitization and validation of a chemical synthesis literature database in the chempu. Science 377, 172–180 (2022).

    Article  Google Scholar 

  121. Raccuglia, P. et al. Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73–76 (2016).

    Article  Google Scholar 

  122. Wen, M., Afshar, Y., Elliott, R. S. & Tadmor, E. B. KLIFF: a framework to develop physics-based and machine learning interatomic potentials. Comput. Phys. Commun. 272, 108218 (2022).

    Article  Google Scholar 

  123. St John, P. C., Guan, Y., Kim, Y., Kim, S. & Paton, R. S. Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nat. Commun. 11, 2328 (2020).

  124. Wen, M., Blau, S. M., Spotte-Smith, E. W. C., Dwaraknath, S. & Persson, K. A. BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules. Chem. Sci. 12, 1858–1868 (2020).

    Article  Google Scholar 

  125. Grambow, C. A., Pattanaik, L. & Green, W. H. Deep learning of activation energies. J. Phys. Chem. Lett. 11, 2992–2997 (2020).

    Article  Google Scholar 

  126. Heinen, S., von Rudorff, G. F. & von Lilienfeld, O. A. Toward the design of chemical reactions: machine learning barriers of competing mechanisms in reactant space. J. Chem. Phys. 155, 064105 (2021).

    Article  Google Scholar 

  127. Houston, P. L., Nandi, A. & Bowman, J. M. A machine learning approach for prediction of rate constants. J. Phys. Chem. Lett. 10, 5250–5258 (2019).

    Article  Google Scholar 

  128. Jorner, K., Brinck, T., Norrby, P.-O. & Buttar, D. Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies. Chem. Sci. 12, 1163–1175 (2021).

    Article  Google Scholar 

  129. Gastegger, M., Schütt, K. T. & Müller, K.-R. Machine learning of solvent effects on molecular spectra and reactions. Chem. Sci. 12, 11473–11483 (2021).

    Article  Google Scholar 

  130. Kim, S., Ji, W., Deng, S., Ma, Y. & Rackauckas, C. Stiff neural ordinary differential equations. Chaos 31, 093122 (2021).

    Article  Google Scholar 

  131. Raissi, M., Perdikaris, P. & Karniadakis, G. E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019).

    Article  MATH  Google Scholar 

  132. Ji, W., Qiu, W., Shi, Z., Pan, S. & Deng, S. Stiff-PINN: physics-informed neural network for stiff chemical kinetics. J. Phys. Chem. A 125, 8098–8106 (2021).

    Article  Google Scholar 

  133. Wang, S., Teng, Y. & Perdikaris, P. Understanding and mitigating gradient flow pathologies in physics-informed neural networks. SIAM J. Sci. Comput. 43, 3055–3081 (2021).

    Article  MATH  Google Scholar 

  134. Krishnapriyan, A., Gholami, A., Zhe, S., Kirby, R. & Mahoney, M. W. Characterizing possible failure modes in physics-informed neural networks. Adv. Neural Inf. Process. Syst. 34, 26548–26560 (2021).

  135. Krishnapriyan, A. S., Queiruga, A. F., Erichson, N. B. & Mahoney, M. W. Learning continuous models for continuous physics. Preprint at https://arxiv.org/abs/2202.08494 (2022).

  136. Queiruga, A. F., Erichson, N. B., Taylor, D. & Mahoney, M.W. Continuous-in-depth neural networks. Preprint at https://arxiv.org/abs/2008.02389 (2020).

  137. Amos, B., Jimenez, I., Sacks, J., Boots, B. & Kolter, J. Z. Differentiable MPC for end-to-end planning and control. Adv. Neural Inf. Process. Syst. 31, 8299–8310 (2018).

  138. Négiar, G., Mahoney, M. W. & Krishnapriyan, A. S. Learning differentiable solvers for systems with hard constraints. Preprint at https://arxiv.org/abs/2207.08675 (2022).

  139. Lu, L., Jin, P., Pang, G., Zhang, Z. & Karniadakis, G. E. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell. 3, 218–229 (2021).

    Article  Google Scholar 

  140. Kovachki, N. et al. Neural operator: learning maps between function spaces. Preprint at https://arxiv.org/abs/2108.08481 (2022).

  141. Gilpin, W. Chaos as an interpretable benchmark for forecasting and data-driven modelling. Preprint at https://arxiv.org/abs/2110.05266 (2021).

  142. Snowden, T. J., van der Graaf, P. H. & Tindall, M. J. Methods of model reduction for large-scale biological systems: a survey of current methods and trends. Bull. Math. Biol. 79, 1449–1486 (2017).

    Article  MATH  Google Scholar 

  143. Yang, Q., Sing-Long, C. A. & Reed, E. J. Learning reduced kinetic monte carlo models of complex chemistry from molecular dynamics. Chem. Sci. 8, 5781–5796 (2017).

    Article  Google Scholar 

  144. Hoffmann, M., Fröhner, C. & Noé, F. Reactive SINDy: discovering governing reactions from concentration data. J. Chem. Phys. 150, 025101 (2019).

    Article  Google Scholar 

  145. Katsoulakis, M. A. & Vilanova, P. Data-driven, variational model reduction of high-dimensional reaction networks. J. Comput. Phys. 401, 108997 (2020).

    Article  MATH  Google Scholar 

  146. Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112, 859–877 (2017).

    Article  Google Scholar 

  147. Wang, Z. et al. A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics. Preprint at https://arxiv.org/abs/2201.02025 (2022).

  148. Singh, P. & Hellander, A. Surrogate assisted model reduction for stochastic biochemical reaction networks. In 2017 Winter Simulation Conference 1773–1783 (IEEE, 2017); https://doi.org/10.1109/WSC.2017.8247915

  149. Chu, T.-C., Smith, M. C., Yang, J., Liu, M. & Green, W. H. Theoretical study on the HACA chemistry of naphthalenyl radicals and acetylene: the formation of C12H8, C14H8, and C14H10 species. Int. J. Chem. Kinet. 52, 752–768 (2020).

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

This work is intellectually led by the Silicon Consortium Project directed by B. Cunningham under the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Vehicle Technologies of the US Department of Energy, contract number DE-AC02-05CH11231 (M.W. and E.W.C.S.-S.) with additional support from the start-up funds from the Presidential Frontier Faculty Program at the University of Houston (M.W.), the Joint Center for Energy Storage Research, an Energy Innovation Hub funded by the US Department of Energy, Office of Science, Basic Energy Sciences (E.W.C.S.-S.), the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under US Department of Energy contract number DE-AC02-05CH11231 (S.M.B.), GENESIS: A Next Generation Synthesis Center, an Energy Frontier Research Center funded by the US Department of Energy, Office of Science, Basic Energy Sciences under award number DE-SC0019212 (M.J.M.), and the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) programme under contract number DE-AC02-05CH11231 (A.S.K.).

Author information

Authors and Affiliations

  1. Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA

    Mingjian Wen

  2. Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Mingjian Wen & Samuel M. Blau

  3. Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Evan Walter Clark Spotte-Smith & Matthew J. McDermott

  4. Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, USA

    Evan Walter Clark Spotte-Smith, Matthew J. McDermott & Kristin A. Persson

  5. Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Aditi S. Krishnapriyan

  6. Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA, USA

    Aditi S. Krishnapriyan

  7. Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA

    Aditi S. Krishnapriyan

  8. Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Kristin A. Persson

Authors
  1. Mingjian Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Evan Walter Clark Spotte-Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samuel M. Blau
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matthew J. McDermott
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aditi S. Krishnapriyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kristin A. Persson
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, investigation: M.W., E.W.C.S.-S., S.M.B., M.J.M. and A.S.K.; writing—original draft: M.W., E.W.C.S.-S., M.J.M. and A.S.K.; writing—review and editing: M.W., E.W.C.S.-S., S.M.B., M.J.M., A.S.K. and K.A.P.; visualization: M.W. and S.M.B.; project administration: M.W. and S.M.B.; funding acquisition: M.W., S.M.B., A.S.K. and K.A.P.; supervision: S.M.B. and K.A.P.

Corresponding author

Correspondence to Kristin A. Persson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Computational Science thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wen, M., Spotte-Smith, E.W.C., Blau, S.M. et al. Chemical reaction networks and opportunities for machine learning. Nat Comput Sci 3, 12–24 (2023). https://doi.org/10.1038/s43588-022-00369-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-022-00369-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing