1932

Abstract

Predicting the whole process of a chemical reaction while solving kinetic equations presents an opportunity to realize an on-the-fly kinetic simulation that directly discovers chemical reactions with their product yields. Such a simulation avoids the combinatorial explosion of reaction patterns to be examined by narrowing the search space based on the kinetic analysis of the reaction path network, and would open a new paradigm beyond the conventional two-step approach, which requires a reaction path network prior to performing a kinetic simulation. The authors addressed this issue and developed a practical method by combining the artificial force induced reaction method with the rate constant matrix contraction method. Two algorithms are available for this purpose: a forward mode with reactants as the input and a backward mode with products as the input. This article first numerically verifies these modes for known reactions and then demonstrates their application to the actual reaction discovery. Finally, the challenges of this method and the prospects for ab initio reaction discovery are discussed.

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2023-04-24
2024-12-05
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Literature Cited

  1. 1.
    Corey EJ, Wipke WT. 1969. Computer-assisted design of complex organic syntheses: pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166:178–92
    [Google Scholar]
  2. 2.
    Cook A, Johnson AP, Law J, Mirzazadeh M, Ravitz O, Simon A. 2012. Computer-aided synthesis design: 40 years on. WIREs Comput. Mol. Sci. 2:79–107
    [Google Scholar]
  3. 3.
    Szymkuć S, Gajewska EP, Klucznik T, Molga K, Dittwald P et al. 2016. Computer-assisted synthetic planning: the end of the beginning. Angew. Chem. Int. Ed. 55:5904–37
    [Google Scholar]
  4. 4.
    Segler MHS, Preuss M, Waller MP. 2018. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555:604–10
    [Google Scholar]
  5. 5.
    Coley CW, Green WH, Jensen KF. 2018. Machine learning in computer-aided synthesis planning. Acc. Chem. Res. 51:1281–89
    [Google Scholar]
  6. 6.
    Sigman MS, Harper KC, Bess EN, Milo A 2016. The development of multidimensional analysis tools for asymmetric catalysis and beyond. Acc. Chem. Res. 49:1292–301
    [Google Scholar]
  7. 7.
    Reizman BJ, Jensen KF. 2016. Feedback in flow for accelerated reaction development. Acc. Chem. Res. 49:1786–96
    [Google Scholar]
  8. 8.
    Zahrt AF, Henle JJ, Rose BT, Wang Y, Darrow WT, Denmark S. 2019. Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Science 363:eaau5631
    [Google Scholar]
  9. 9.
    Shields BJ, Stevens J, Li J, Parasram M, Damani F et al. 2021. Bayesian reaction optimization as a tool for chemical synthesis. Nature 590:89–96
    [Google Scholar]
  10. 10.
    Shen Y, Borowski JE, Hardy MA, Sarpong R, Doyle AG, Cernak T. 2021. Automation and computer-assisted planning for chemical synthesis. Nat. Rev. Methods Primers 1:24
    [Google Scholar]
  11. 11.
    Robbins DW, Hartwig JF. 2011. A simple, multidimensional approach to high-throughput discovery of catalytic reactions. Science 333:1423–27
    [Google Scholar]
  12. 12.
    Granda JM, Donina L, Dragone V, Long DL, Cronin L. 2018. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559:377–81
    [Google Scholar]
  13. 13.
    Schlegel HB. 2003. Exploring potential energy surfaces for chemical reactions: an overview of some practical methods. J. Comput. Chem. 24:1514–27
    [Google Scholar]
  14. 14.
    Wales DJ. 2003. Energy Landscapes: With Applications to Clusters, Biomolecules and Glasses Cambridge, UK: Cambridge University Press
    [Google Scholar]
  15. 15.
    Fernández-Ramos A, Miller JA, Klippenstein SJ, Truhlar DG. 2006. Modeling the kinetics of bimolecular reactions. Chem. Rev. 106:4518–84
    [Google Scholar]
  16. 16.
    Houk KN, Cheong PHY. 2008. Computational prediction of small-molecule catalysts. Nature 455:309–13
    [Google Scholar]
  17. 17.
    Thiel W. 2014. Computational catalysis—past, present, and future. Angew. Chem. Int. Ed. 53:8605–13
    [Google Scholar]
  18. 18.
    Sameera WMC, Maeda S, Morokuma K. 2016. Computational catalysis using the artificial force induced reaction method. Acc. Chem. Res. 49:763–73
    [Google Scholar]
  19. 19.
    Houk KN, Liu F. 2017. Holy grails for computational organic chemistry and biochemistry. Acc. Chem. Res. 50:539–43
    [Google Scholar]
  20. 20.
    Ahn S, Hong M, Sundararajan M, Ess DH, Baik MH. 2019. Design and optimization of catalysts based on mechanistic in-sights derived from quantum chemical reaction modeling. Chem. Rev. 119:6509–60
    [Google Scholar]
  21. 21.
    Schlegel HB. 2011. Geometry optimization. WIREs Comput. Mol. Sci. 1:790–809
    [Google Scholar]
  22. 22.
    Fukui K. 1981. The path of chemical reactions - the IRC approach. Acc. Chem. Res. 14:363–68
    [Google Scholar]
  23. 23.
    Maeda S, Harabuchi Y, Ono Y, Taketsugu T, Morokuma K. 2015. Intrinsic reaction coordinate: calculation, bifurcation, and automated search. Int. J. Quantum Chem. 115:258–69
    [Google Scholar]
  24. 24.
    Hayes DM, Morokuma K. 1972. Theoretical studies of carbonyl photochemistry. I. Ab initio potential energy surfaces for the photodissociation H2CO*→H + HCO. Chem. Phys. Lett. 12:539–43
    [Google Scholar]
  25. 25.
    Jaffe RL, Hayes DM, Morokuma K. 1974. Photodissociation of formaldehyde: potential energy surfaces for H2CO → H2 + CO. J. Chem. Phys. 60:5108–9
    [Google Scholar]
  26. 26.
    Cerjan CJ, Miller WH. 1981. On finding transition states. J. Chem. Phys. 75:2800–6
    [Google Scholar]
  27. 27.
    Maeda S, Ohno K. 2005. Global mapping of equilibrium and transition structures on potential energy surfaces by the scaled hypersphere search method: applications to ab initio surfaces of formaldehyde and propyne molecules. J. Phys. Chem. A 109:5742–53
    [Google Scholar]
  28. 28.
    Maeda S, Taketsugu T, Morokuma K, Ohno K. 2014. Anharmonic downward distortion following for automated exploration of quantum chemical potential energy surfaces. Bull. Chem. Soc. Jpn. 87:1315–34
    [Google Scholar]
  29. 29.
    Maeda S, Morokuma K. 2010. Communications: a systematic method for locating transition structures of A + B → X type reactions. J. Chem. Phys. 132:241102
    [Google Scholar]
  30. 30.
    Maeda S, Harabuchi Y. 2021. Exploring paths of chemical transformations in molecular and periodic systems: an approach utilizing force. WIREs Comput. Mol. Sci. 11:e1538
    [Google Scholar]
  31. 31.
    Halgren TA, Lipscomb WN. 1977. The synchronous-transit method for determining reaction pathways and locating molecular transition states. Chem. Phys. Lett. 49:225–32
    [Google Scholar]
  32. 32.
    Dewar MJS, Healy EF, Stewart JJP. 1984. Location of transition states in reaction mechanisms. J. Chem. Soc. Faraday Trans. 2 80:227–33
    [Google Scholar]
  33. 33.
    Ionova IV, Carter EA. 1993. Ridge method for finding saddle points on potential energy surfaces. J. Chem. Phys. 98:6377–86
    [Google Scholar]
  34. 34.
    Maeda S, Ohno K. 2005. A new approach for finding a transition state connecting a reactant and a product without initial guess: applications of the scaled hypersphere search method to isomerization reactions of HCN, (H2O)2, and alanine dipeptide. Chem. Phys. Lett. 404:95–99
    [Google Scholar]
  35. 35.
    Maeda S, Harabuchi Y, Takagi M, Saita K, Suzuki K et al. 2018. Implementation and performance of the artificial force induced reaction method in the GRRM17 program. J. Comput. Chem. 39:233–51
    [Google Scholar]
  36. 36.
    Henkelman G, Uberuaga BP, Jónsson H. 2000. A climbing image nudged elastic band method for finding saddle points and minimum energy paths. J. Chem. Phys. 113:9901–4
    [Google Scholar]
  37. 37.
    Weinan E, Ren W, Vanden-Eijnden E. 2002. String method for the study of rare events. Phys. Rev. B 66:052301
    [Google Scholar]
  38. 38.
    Choi C, Elber R. 1991. Reaction path study of helix formation in tetrapeptides: effect of side chains. J. Chem. Phys. 94:751–60
    [Google Scholar]
  39. 39.
    Peters B, Heyden A, Bell AT, Chakraborty A. 2004. A growing string method for determining transition states: comparison to the nudged elastic band and string methods. J. Chem. Phys. 120:7877–86
    [Google Scholar]
  40. 40.
    Behn A, Zimmerman PM, Bell AT, Head-Gordon M. 2011. Efficient exploration of reaction paths via a freezing string method. J. Chem. Phys. 135:224108
    [Google Scholar]
  41. 41.
    Zimmerman PM. 2015. Single-ended transition state finding with the growing string method. J. Comput. Chem. 36:601–11
    [Google Scholar]
  42. 42.
    Maeda S, Ohno K. 2008. Lowest transition state for the chirality-determining step in Ru((R)-BINAP)-catalyzed asymmetric hydrogenation of methyl-3-oxobutanoate. J. Am. Chem. Soc. 130:17228–29
    [Google Scholar]
  43. 43.
    Donoghue PJ, Helquist P, Norrby PO, Wiest O. 2009. Prediction of enantioselectivity in rhodium catalyzed hydrogenations. J. Am. Chem. Soc. 131:410–11
    [Google Scholar]
  44. 44.
    Hatanaka M, Maeda S, Morokuma K. 2013. Sampling of transition states for predicting diastereoselectivity using automated search method-aqueous lanthanide-catalyzed Mukaiyama aldol reaction. J. Chem. Theory Comput. 9:2882–86
    [Google Scholar]
  45. 45.
    Hansen E, Rosales AR, Tutkowski B, Norrby PO, Wiest O. 2016. Prediction of stereochemistry using Q2MM. Acc. Chem. Res. 49:996–1005
    [Google Scholar]
  46. 46.
    Guan Y, Ingman VM, Rooks BJ, Wheeler SE. 2018. AARON: an automated reaction optimizer for new catalysts. J. Chem. Theory Comput. 14:5249–61
    [Google Scholar]
  47. 47.
    Hatanaka M, Yoshimura T, Maeda S. 2020. Artificial force induced reaction method for systematic elucidation of mechanism and selectivity in organometallic reactions. Top. Organomet. Chem. 67:57–80
    [Google Scholar]
  48. 48.
    Maeda S, Ohno K, Morokuma K. 2013. Systematic exploration of the mechanism of chemical reactions: the global reaction route mapping (GRRM) strategy using the ADDF and AFIR methods. Phys. Chem. Chem. Phys. 15:3683–701
    [Google Scholar]
  49. 49.
    Dewyer AL, Argüelles AJ, Zimmerman PM. 2018. Methods for exploring reaction space in molecular systems. WIREs Comput. Mol. Sci. 8:e1354
    [Google Scholar]
  50. 50.
    Grambow CA, Jamal A, Li YP, Green WH, Zador J, Suleimanov YV. 2018. Unimolecular reaction pathways of a γ-ketohydroperoxide from combined application of automated reaction discovery methods. J. Am. Chem. Soc. 140:1035–48
    [Google Scholar]
  51. 51.
    Simm GN, Vaucher AC, Reiher M. 2019. Exploration of reaction pathways and chemical transformation networks. J. Phys. Chem. A 123:385–99
    [Google Scholar]
  52. 52.
    Wales DJ, Doye JPK, Miller MA, Mortenson PN, Walsh TR. 2000. Energy landscapes: from clusters to biomolecules. Adv. Chem. Phys. 115:1–112
    [Google Scholar]
  53. 53.
    Černohorský M, Kettou S, Koča J. 1999. VADER: New software for exploring interconversions on potential energy surfaces. J. Chem. Inf. Comput. Sci. 39:705–12
    [Google Scholar]
  54. 54.
    Yang M, Zou J, Wang G, Li S. 2017. Automatic reaction pathway search via combined molecular dynamics and coordinate driving method. J. Phys. Chem. A 121:1351–61
    [Google Scholar]
  55. 55.
    Zimmerman PM. 2013. Automated discovery of chemically reasonable elementary reaction steps. J. Comput. Chem. 34:1385–92
    [Google Scholar]
  56. 56.
    Rappoport D, Galvin CJ, Zubarev DY, Aspuru-Guzik A. 2014. Complex chemical reaction networks from heuristics-aided quantum chemistry. J. Chem. Theory Comput. 10:897–907
    [Google Scholar]
  57. 57.
    Kim Y, Choi S, Kim WY. 2014. Efficient basin-hopping sampling of reaction intermediates through molecular fragmentation and graph theory. J. Chem. Theory Comput. 10:2419–26
    [Google Scholar]
  58. 58.
    Suleimanov YV, Green WH. 2015. Automated discovery of elementary chemical reaction steps using freezing string and Berny optimization methods. J. Chem. Theory Comput. 11:4248–59
    [Google Scholar]
  59. 59.
    Bergeler M, Simm GN, Proppe J, Reiher M. 2015. Heuristics-guided exploration of reaction mechanisms. J. Chem. Theory Comput. 11:5712–22
    [Google Scholar]
  60. 60.
    Ulissi ZW, Medford AJ, Bligaard T, Nørskov JK. 2017. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat. Commun. 8:14621
    [Google Scholar]
  61. 61.
    Simm GN, Reiher M. 2017. Context-driven exploration of complex chemical reaction networks. J. Chem. Theory Comput. 13:6108–19
    [Google Scholar]
  62. 62.
    Wang LP, Titov A, McGibbon R, Liu F, Pande VS, Martínez TJ. 2014. Discovering chemistry with an ab initio nanoreactor. Nat. Chem. 6:1044–48
    [Google Scholar]
  63. 63.
    Martínez-Núñez E. 2015. An automated method to find transition states using chemical dynamics simulations. J. Comput. Chem. 36:222–34
    [Google Scholar]
  64. 64.
    Van de Vijver R, Zádor J. 2020. KinBot: automated stationary point search on potential energy surfaces. Comput. Phys. Commun. 248:106947
    [Google Scholar]
  65. 65.
    Sumiya Y, Nagahata Y, Komatsuzaki T, Taketsugu T, Maeda S. 2015. Kinetic analysis for the multistep profiles of organic reactions: significance of the conformational entropy on the rate constants of the Claisen rearrangement. J. Phys. Chem. A 119:11641–49
    [Google Scholar]
  66. 66.
    Sumiya Y, Maeda S. 2020. Rate constant matrix contraction method for systematic analysis of reaction path networks. Chem. Lett. 49:553–64
    [Google Scholar]
  67. 67.
    Sumiya Y, Harabuchi Y, Nagata Y, Maeda S. 2022. Quantum chemical calculations to trace back reaction paths for the prediction of reactants. JACS Au 2:1181–88
    [Google Scholar]
  68. 68.
    Kozuch S, Shaik S. 2010. How to conceptualize catalytic cycles? The energetic span model. Acc. Chem. Res. 44:101–10
    [Google Scholar]
  69. 69.
    Sumiya Y, Taketsugu T, Maeda S. 2017. Full rate constant matrix contraction method for obtaining branching ratio of unimolecular decomposition. J. Comput. Chem. 38:101–9
    [Google Scholar]
  70. 70.
    Ford J, Seritan S, Zhu X, Sakano MN, Islam MM et al. 2021. Nitromethane decomposition via automated reaction discovery and an ab initio corrected kinetic model. J. Phys. Chem. A 125:1447–60
    [Google Scholar]
  71. 71.
    Garay-Ruiz D, Álvarez-Moreno M, Bo C, Martínez-Núñez E. 2022. New tools for taming complex reaction networks: the unimolecular decomposition of indole revisited. ACS Phys. Chem. Au 2:225–36
    [Google Scholar]
  72. 72.
    Ong MT, Leiding J, Tao H, Virshup AM, Martínez TJ. 2009. First principles dynamics and minimum energy pathways for mechanochemical ring opening of cyclobutene. J. Am. Chem. Soc. 131:6377–79
    [Google Scholar]
  73. 73.
    Caruso MM, Davis DA, Shen Q, Odom SA, Sottos NR et al. 2009. Mechanically-induced chemical changes in polymeric materials. Chem. Rev. 109:5755–98
    [Google Scholar]
  74. 74.
    Maeda S, Morokuma K. 2011. Finding reaction pathways of type A + B → X: toward systematic prediction of reaction mechanisms. J. Chem. Theory Comput. 7:2335–45
    [Google Scholar]
  75. 75.
    Maeda S, Taketsugu T, Morokuma K. 2014. Exploring transition state structures for intramolecular pathways by the artificial force induced reaction method. J. Comput. Chem. 35:166–73
    [Google Scholar]
  76. 76.
    Sumiya Y, Maeda S. 2019. A reaction path network for Wohler's urea synthesis. Chem. Lett. 48:47–50
    [Google Scholar]
  77. 77.
    Maeda S, Harabuchi Y, Hasegawa T, Suzuki K, Mita T. 2021. Reactivity prediction through quantum chemical calculations. AsiaChem Mag 2:56–63
    [Google Scholar]
  78. 78.
    Corey EJ. 1991. The logic of chemical synthesis: multistep synthesis of complex carbogenic molecules. Angew. Chem. Int. Ed. 30:455–65
    [Google Scholar]
  79. 79.
    Mita T, Harabuchi Y, Maeda S. 2020. Discovery of a synthesis method for a difluoroglycine derivative based on a path generated by quantum chemical calculations. Chem. Sci. 11:7569–77
    [Google Scholar]
  80. 80.
    Harabuchi Y, Maeda S. 2022. Theoretical chemical reaction database construction based on quantum chemistry-aided retrosynthetic analysis. ChemRxiv 2022-tl4vj. https://doi.org/10.26434/chemrxiv-2022-tl4vj
  81. 81.
    Hayashi H, Takano H, Katsuyama H, Harabuchi Y, Maeda S, Mita T. 2021. Synthesis of difluoroglycine derivatives from amines, difluorocarbene, and CO2: computational design, scope, and applications. Chem.-Eur. J. 27:10040–47
    [Google Scholar]
  82. 82.
    Hayashi H, Katsuyama H, Takano H, Harabuchi Y, Maeda S, Mita T. 2022. In silico reaction screening with difluorocarbene for N-difluoroalkylative dearomatization of pyridines. Nat. Synth. 1:804–14
    [Google Scholar]
  83. 83.
    Hada M, Hasegawa T, Inoue H, Takagi M, Omoto K et al. 2019. One-minute Joule annealing enhances the thermoelectric properties of carbon nanotube yarns via the formation of graphene at the interface. ACS Appl. Energy Mater. 2:7700–8
    [Google Scholar]
  84. 84.
    Sugiyama K, Sumiya Y, Takagi M, Saita K, Maeda S. 2019. Understanding CO oxidation on Pt(111) surface based on reaction route network. Phys. Chem. Chem. Phys. 21:14366–75
    [Google Scholar]
  85. 85.
    Sugiyama K, Saita K, Maeda S. 2021. A reaction route network for methanol decomposition on a Pt(111) surface. J. Comput. Chem. 42:2163–69
    [Google Scholar]
  86. 86.
    Matsuoka W, Harabuchi Y, Maeda S. 2022. Virtual-ligand-assisted screening strategy to discover enabling ligands for transition metal catalysis. ACS Catal 12:3752–66
    [Google Scholar]
  87. 87.
    Harabuchi Y, Hayashi H, Takano H, Mita T, Maeda S. 2023. Oxidation and reduction pathways in the Knowles hydroamination via a photoredox-catalyzed radical reaction. Angew. Chem. Int. Ed. 62:e202211936
    [Google Scholar]
  88. 88.
    Maeda S, Ohno K, Morokuma K. 2009. An automated and systematic transition structure explorer in large flexible molecular systems based on combined global reaction route mapping and microiteration methods. J. Chem. Theory Comput. 5:2734–43
    [Google Scholar]
  89. 89.
    Nakao A, Harabuchi Y, Maeda S, Tsuda K. 2022. Leveraging algorithmic search in quantum chemical reaction path finding. Phys. Chem. Chem. Phys. 24:10305–10
    [Google Scholar]
  90. 90.
    Smith JS, Nebgen BT, Zubatyuk R, Lubbers N, Devereux C et al. 2019. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. Nat. Commun. 10:2903
    [Google Scholar]
  91. 91.
    Li J, Song K, Behler J. 2019. A critical comparison of neural network potentials for molecular reaction dynamics with exact permutation symmetry. Phys. Chem. Chem. Phys. 21:9672–82
    [Google Scholar]
  92. 92.
    Nandi A, Qu C, Houston PL, Conte R, Bowman JM. 2021. Δ-Machine learning for potential energy surfaces: a PIP approach to bring a DFT-based PES to CCSD(T) level of theory. J. Chem. Phys. 154:051102
    [Google Scholar]
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