1932

Abstract

Modern computational chemistry has reached a stage at which massive exploration into chemical reaction space with unprecedented resolution with respect to the number of potentially relevant molecular structures has become possible. Various algorithmic advances have shown that such structural screenings must and can be automated and routinely carried out. This will replace the standard approach of manually studying a selected and restricted number of molecular structures for a chemical mechanism. The complexity of the task has led to many different approaches. However, all of them address the same general target, namely to produce a complete atomistic picture of the kinetics of a chemical process. It is the purpose of this overview to categorize the problems that should be targeted and to identify the principal components and challenges of automated exploration machines so that the various existing approaches and future developments can be compared based on well-defined conceptual principles.

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2020-04-20
2024-04-20
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Literature Cited

  1. 1. 
    Corey EJ, Cheng XM 1995. The Logic of Chemical Synthesis New York: Wiley. 1st ed
  2. 2. 
    Broadbelt LJ, Stark SM, Klein MT 1994. Computer generated pyrolysis modeling: on-the-fly generation of species, reactions, and rates. Ind. Eng. Chem. Res. 33:790–99
    [Google Scholar]
  3. 3. 
    Broadbelt LJ, Stark S, Klein M 1996. Computer generated reaction modelling: decomposition and encoding algorithms for determining species uniqueness. Comput. Chem. Eng. 20:113–29
    [Google Scholar]
  4. 4. 
    Broadbelt LJ, Pfaendtner J 2005. Lexicography of kinetic modeling of complex reaction networks. AIChE J. 51:2112–21
    [Google Scholar]
  5. 5. 
    Schwaller P, Laino T, Gaudin T, Bolgar P, Hunter CA 2019. Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction. ACS Cent. Sci 51572–83
  6. 6. 
    Schwaller P, Gaudin T, Lnyi D, Bekas C, Laino T 2018. “Found in translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models. Chem. Sci. 9:6091–98
    [Google Scholar]
  7. 7. 
    Segler MHS, Preuss M, Waller MP 2018. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555:604–10
    [Google Scholar]
  8. 8. 
    Pensak DA, Corey EJ 1977. LHASA—logic and heuristic applied to synthetic analysis. Computer-Assisted Organic Synthesis WT Wipke, WJ Howe1–32 Washington, DC: Am. Chem. Soc.
  9. 9. 
    Ihlenfeldt WD, Gasteiger J 1996. Computer-assisted planning of organic syntheses: the second generation of programs. Angew. Chem. Int. Ed. 34:2613–33
    [Google Scholar]
  10. 10. 
    Gothard CM, Soh S, Gothard NA, Kowalczyk B, Wei Y et al. 2012. Rewiring chemistry: algorithmic discovery and experimental validation of one-pot reactions in the network of organic chemistry. Angew. Chem. Int. Ed. 51:7922–27
    [Google Scholar]
  11. 11. 
    Kowalik M, Gothard CM, Drews AM, Gothard NA, Weckiewicz A et al. 2012. Parallel optimization of synthetic pathways within the network of organic chemistry. Angew. Chem. Int. Ed. 51:7928–32
    [Google Scholar]
  12. 12. 
    Segler MHS, Waller MP 2017. Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chem. Eur. J. 23:5966–71
    [Google Scholar]
  13. 13. 
    Coley CW, Rogers L, Green WH, Jensen KF 2017. Computer-assisted retrosynthesis based on molecular similarity. ACS Cent. Sci. 3:1237–45
    [Google Scholar]
  14. 14. 
    Coley CW, Barzilay R, Jaakkola TS, Green WH, Jensen KF 2017. Prediction of organic reaction outcomes using machine learning. ACS Cent. Sci. 3:434–43
    [Google Scholar]
  15. 15. 
    Segler MHS, Waller MP 2017. Modelling chemical reasoning to predict and invent reactions. Chem. Eur. J. 23:6118–28
    [Google Scholar]
  16. 16. 
    Coley CW, Green WH, Jensen KF 2018. Machine learning in computer-aided synthesis planning. Acc. Chem. Res. 51:1281–89
    [Google Scholar]
  17. 17. 
    Simm GN, Vaucher AC, Reiher M 2019. Exploration of reaction pathways and chemical transformation networks. J. Phys. Chem. A 123:385–99
    [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. 
    Dewyer AL, Argüelles AJ, Zimmerman PM 2018. Methods for exploring reaction space in molecular systems. WIREs Comput. Mol. Sci. 8:e1354
    [Google Scholar]
  20. 20. 
    Habershon S 2016. Automated prediction of catalytic mechanism and rate law using graph-based reaction path sampling. J. Chem. Theory Comput. 12:1786–98
    [Google Scholar]
  21. 21. 
    Kim Y, Kim JW, Kim Z, Kim WY 2018. Efficient prediction of reaction paths through molecular graph and reaction network analysis. Chem. Sci. 9:825–35
    [Google Scholar]
  22. 22. 
    Ismail I, Stuttaford-Fowler HBVA, Ochan Ashok C, Robertson C, Habershon S 2019. Automatic proposal of multistep reaction mechanisms using a graph-driven search. J. Phys. Chem. A 123:3407–17
    [Google Scholar]
  23. 23. 
    Bergeler M, Simm GN, Proppe J, Reiher M 2015. Heuristics-guided exploration of reaction mechanisms. J. Chem. Theory Comput. 11:5712–22
    [Google Scholar]
  24. 24. 
    Simm GN, Reiher M 2017. Context-driven exploration of complex chemical reaction networks. J. Chem. Theory Comput. 13:6108–19
    [Google Scholar]
  25. 25. 
    Grimmel S, Reiher M 2019. The electrostatic potential as a descriptor for the protonation propensity in automated exploration of reaction mechanisms. Faraday Discuss. 220443–63
  26. 26. 
    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]
  27. 27. 
    Rappoport D, Aspuru-Guzik A 2019. Predicting feasible organic reaction pathways using heuristically aided quantum chemistry. J. Chem. Theory Comput. 1574099–112
  28. 28. 
    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]
  29. 29. 
    Habershon S 2015. Sampling reactive pathways with random walks in chemical space: applications to molecular dissociation and catalysis. J. Chem. Phys. 143:094106
    [Google Scholar]
  30. 30. 
    Ohno K, Maeda S 2004. A scaled hypersphere search method for the topography of reaction pathways on the potential energy surface. Chem. Phys. Lett. 384:277–82
    [Google Scholar]
  31. 31. 
    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]
  32. 32. 
    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]
  33. 33. 
    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]
  34. 34. 
    Zimmerman PM 2015. Single-ended transition state finding with the growing string method. J. Comput. Chem. 36:601–11
    [Google Scholar]
  35. 35. 
    Dewyer AL, Zimmerman PM 2017. Finding reaction mechanisms, intuitive or otherwise. Org. Biomol. Chem. 15:501–4
    [Google Scholar]
  36. 36. 
    Wang LP, Titov A, McGibbon R, Liu F, Pande VS, Martínez TJ 2014. Discovering chemistry with an ab initio nanoreactor. Nat. Chem. 6:1044
    [Google Scholar]
  37. 37. 
    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]
  38. 38. 
    Huber T, Torda AE, van Gunsteren WF 1994. Local elevation: a method for improving the searching properties of molecular dynamics simulation. J. Comput.-Aided Mol. Des. 8:695–708
    [Google Scholar]
  39. 39. 
    Laio A, Parrinello M 2002. Escaping free-energy minima. PNAS 99:12562–66
    [Google Scholar]
  40. 40. 
    Grimme S 2019. Exploration of chemical compound, conformer, and reaction space with meta-dynamics simulations based on tight-binding quantum chemical calculations. J. Chem. Theory Comput. 15:2847–62
    [Google Scholar]
  41. 41. 
    Rizzi V, Mendels D, Sicilia E, Parrinello M 2019. Blind search for complex chemical pathways using harmonic linear discriminant analysis. J. Chem. Theory Comput. 1584507–15
  42. 42. 
    Martínez-Núñez E 2015. An automated method to find transition states using chemical dynamics simulations. J. Comput. Chem. 36:222–34
    [Google Scholar]
  43. 43. 
    Varela JA, Vázquez SA, Martínez-Núñez E 2017. An automated method to find reaction mechanisms and solve the kinetics in organometallic catalysis. Chem. Sci. 8:3843–51
    [Google Scholar]
  44. 44. 
    Yang M, Yang L, Wang G, Zhou Y, Xie D, Li S 2018. Combined molecular dynamics and coordinate driving method for automatic reaction pathway search of reactions in solution. J. Chem. Theory Comput. 14:5787–96
    [Google Scholar]
  45. 45. 
    Debnath J, Invernizzi M, Parrinello M 2019. Enhanced sampling of transition states. J. Chem. Theory Comput. 15:2454–59
    [Google Scholar]
  46. 46. 
    Grambow CA, Jamal A, Li YP, Green WH, Zádor 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]
  47. 47. 
    Maeda S, Harabuchi Y 2019. On benchmarking of automated methods for performing exhaustive reaction path search. J. Chem. Theory Comput. 15:2111–15
    [Google Scholar]
  48. 48. 
    Green WH, Moore CB, Polik WF 1992. Transition states and rate constants for unimolecular reactions. Annu. Rev. Phys. Chem. 43:591–626
    [Google Scholar]
  49. 49. 
    Susnow RG, Dean AM, Green WH, Peczak P, Broadbelt LJ 1997. Rate-based construction of kinetic models for complex systems. J. Phys. Chem. A 101:3731–40
    [Google Scholar]
  50. 50. 
    Sumathi R, Green WH Jr 2002. A priori rate constants for kinetic modeling. Theor. Chem. Acc. 108:187–213
    [Google Scholar]
  51. 51. 
    Prinz JH, Wu H, Sarich M, Keller B, Senne M et al. 2011. Markov models of molecular kinetics: generation and validation. J. Chem. Phys. 134:174105
    [Google Scholar]
  52. 52. 
    Sabbe MK, Reyniers MF, Reuter K 2012. First-principles kinetic modeling in heterogeneous catalysis: an industrial perspective on best-practice, gaps and needs. Catal. Sci. Technol. 2:2010–24
    [Google Scholar]
  53. 53. 
    Bowman GR, Pande VS, Noé F 2014. An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation. . Dordrecht, Neth: Springer Sci. Bus. Med.
  54. 54. 
    Van de Vijver R, Vandewiele NM, Bhoorasingh PL, Slakman BL, Seyedzadeh Khanshan F et al. 2015. Automatic mechanism and kinetic model generation for gas- and solution-phase processes: a perspective on best practices, recent advances, and future challenges. Int. J. Chem. Kinet. 47:199–231
    [Google Scholar]
  55. 55. 
    Gao CW, Allen JW, Green WH, West RH 2016. Reaction mechanism generator: automatic construction of chemical kinetic mechanisms. Comput. Phys. Commun. 203:212–25
    [Google Scholar]
  56. 56. 
    Wu H, Paul F, Wehmeyer C, Noé F 2016. Multiensemble Markov models of molecular thermodynamics and kinetics. PNAS 113:E3221–30
    [Google Scholar]
  57. 57. 
    Proppe J, Husch T, Simm GN, Reiher M 2016. Uncertainty quantification for quantum chemical models of complex reaction networks. Faraday Discuss. 195:497–520
    [Google Scholar]
  58. 58. 
    Döpking S, Matera S 2017. Error propagation in first-principles kinetic Monte Carlo simulation. Chem. Phys. Lett. 67428–32
  59. 59. 
    Han K, Green WH 2018. A fragment-based mechanistic kinetic modeling framework for complex systems. Ind. Eng. Chem. Res. 57:14022–30
    [Google Scholar]
  60. 60. 
    Vereecken L, Aumont B, Barnes I, Bozzelli J, Goldman M et al. 2018. Perspective on mechanism development and structure-activity relationships for gas-phase atmospheric chemistry. Int. J. Chem. Kinet. 50:435–69
    [Google Scholar]
  61. 61. 
    Scherer MK, Husic BE, Hoffmann M, Paul F, Wu H, No F 2019. Variational selection of features for molecular kinetics. J. Chem. Phys. 150:194108
    [Google Scholar]
  62. 62. 
    Andersen M, Panosetti C, Reuter K 2019. A practical guide to surface kinetic Monte Carlo simulations. Front. Chem. 7:202
    [Google Scholar]
  63. 63. 
    Proppe J, Reiher M 2019. Mechanism deduction from noisy chemical reaction networks. J. Chem. Theory Comput. 15:357–70
    [Google Scholar]
  64. 64. 
    Cavallotti C, Pelucchi M, Georgievskii Y, Klippenstein SJ 2019. EStokTP: electronic structure to temperature- and pressure-dependent rate constants-A code for automatically predicting the thermal kinetics of reactions. J. Chem. Theory Comput. 15:1122–45
    [Google Scholar]
  65. 65. 
    Eyring H 1935. The activated complex in chemical reactions. J. Chem. Phys. 3:107–15
    [Google Scholar]
  66. 66. 
    Kramers H 1940. Brownian motion in a field of force and the diffusion model of chemical reactions. Physica 7:284–304
    [Google Scholar]
  67. 67. 
    Hänggi P, Talkner P, Borkovec M 1990. Reaction-rate theory: fifty years after Kramers. Rev. Mod. Phys. 62:251–341
    [Google Scholar]
  68. 68. 
    Weymuth T, Reiher M 2014. Inverse quantum chemistry: concepts and strategies for rational compound design. Int. J. Quantum Chem. 114:823–37
    [Google Scholar]
  69. 69. 
    Sanchez-Lengeling B, Aspuru-Guzik A 2018. Inverse molecular design using machine learning: generative models for matter engineering. Science 361:360–65
    [Google Scholar]
  70. 70. 
    Freeze JG, Kelly HR, Batista VS 2019. Search for catalysts by inverse design: artificial intelligence, mountain climbers, and alchemists. Chem. Rev. 119:6595–612
    [Google Scholar]
  71. 71. 
    Collins KD, Rühling A, Glorius F 2014. Application of a robustness screen for the evaluation of synthetic organic methodology. Nat. Prot. 9:1348
    [Google Scholar]
  72. 72. 
    Mayer I 1983. Charge, bond order and valence in the ab initio SCF theory. Chem. Phys. Lett. 97:270–74
    [Google Scholar]
  73. 73. 
    Karpen ME, Tobias DJ, Brooks CL 1993. Statistical clustering techniques for the analysis of long molecular dynamics trajectories: analysis of 2.2-ns trajectories of YPGDV. Biochemistry 32:412–20
    [Google Scholar]
  74. 74. 
    Shenkin PS, McDonald DQ 1994. Cluster analysis of molecular conformations. J. Comput. Chem. 15:899–916
    [Google Scholar]
  75. 75. 
    Jain AK, Murty MN, Flynn PJ 1999. Data clustering: a review. ACM Comput. Surv. 31:264–323
    [Google Scholar]
  76. 76. 
    Shao J, Tanner SW, Thompson N, Cheatham TE 2007. Clustering molecular dynamics trajectories: 1. Characterizing the performance of different clustering algorithms. J. Chem. Theory Comput. 3:2312–34
    [Google Scholar]
  77. 77. 
    Keller B, Daura X, van Gunsteren WF 2010. Comparing geometric and kinetic cluster algorithms for molecular simulation data. J. Chem. Phys. 132:074110
    [Google Scholar]
  78. 78. 
    Singhal N, Snow CD, Pande VS 2004. Using path sampling to build better Markovian state models: predicting the folding rate and mechanism of a tryptophan zipper beta hairpin. J. Chem. Phys. 121:415–25
    [Google Scholar]
  79. 79. 
    Bowman GR, Huang X, Pande VS 2009. Using generalized ensemble simulations and Markov state models to identify conformational states. Methods 49:197–201
    [Google Scholar]
  80. 80. 
    Li Y, Dong Z 2016. Effect of clustering algorithm on establishing Markov state model for molecular dynamics simulations. J. Chem. Inf. Model. 56:1205–15
    [Google Scholar]
  81. 81. 
    Vaucher AC, Reiher M 2016. Molecular propensity as a driver for explorative reactivity studies. J. Chem. Inf. Model. 56:1470–78
    [Google Scholar]
  82. 82. 
    Simm GN, Proppe J, Reiher M 2017. Error assessment of computational models in chemistry. CHIMIA 71:202–8
    [Google Scholar]
  83. 83. 
    Simm GN, Reiher M 2018. Error-controlled exploration of chemical reaction networks with Gaussian processes. J. Chem. Theory Comput. 14:5238–48
    [Google Scholar]
  84. 84. 
    Wang LP, Song C 2019. Car-Parrinello monitor for more robust Born-Oppenheimer molecular dynamics. . J. Chem. Theory Comput. 1584454–67
  85. 85. 
    Vaucher AC, Reiher M 2017. Steering orbital optimization out of local minima and saddle points toward lower energy. J. Chem. Theory Comput. 13:1219–28
    [Google Scholar]
  86. 86. 
    Hawkins PCD 2017. Conformation generation: the state of the art. J. Chem. Inf. Model. 57:1747–56
    [Google Scholar]
  87. 87. 
    Klamt A 1995. Conductor-like screening model for real solvents: a new approach to the quantitative calculation of solvation phenomena. J. Phys. Chem. 99:2224–35
    [Google Scholar]
  88. 88. 
    Grambow CA, Li YP, Green WH 2019. Accurate thermochemistry with small data sets: a bond additivity correction and transfer learning approach. J. Phys. Chem. A 123275826–35
  89. 89. 
    Sidler D, Schwaninger A, Riniker S 2016. Replica exchange enveloping distribution sampling (re-eds): a robust method to estimate multiple free-energy differences from a single simulation. J. Chem. Phys. 145:154114
    [Google Scholar]
  90. 90. 
    Sidler D, Cristfol-Clough M, Riniker S 2017. Efficient round-trip time optimization for replica-exchange enveloping distribution sampling (re-eds). J. Chem. Theory Comput. 13:3020–30
    [Google Scholar]
  91. 91. 
    Gomes ASP, Jacob CR 2012. Quantum-chemical embedding methods for treating local electronic excitations in complex chemical systems. Annu. Rep. Prog. Chem., Sect. C: Phys. Chem. 108:222–77
    [Google Scholar]
  92. 92. 
    Jacob CR, Neugebauer J 2014. Subsystem density-functional theory. WIREs Comput. Mol. Sci. 4:325–62
    [Google Scholar]
  93. 93. 
    Wesolowski TA, Shedge S, Zhou X 2015. Frozen-density embedding strategy for multilevel simulations of electronic structure. Chem. Rev. 115:5891–928
    [Google Scholar]
  94. 94. 
    Lee SJR, Welborn M, Manby FR, Miller TF 2019. Projection-based wavefunction-in-DFT embedding. Acc. Chem. Res. 52:1359–68
    [Google Scholar]
  95. 95. 
    Husch T, Vaucher AC, Reiher M 2018. Semiempirical molecular orbital models based on the neglect of diatomic differential overlap approximation. Int. J. Quantum Chem. 118:e25799
    [Google Scholar]
  96. 96. 
    Parr RG, Yang W 1994.Density-Functional Theory of Atoms and Molecules New York: Oxford Univ. Press
  97. 97. 
    Weymuth T, Couzijn EPA, Chen P, Reiher M 2014. New benchmark set of transition-metal coordination reactions for the assessment of density functionals. J. Chem. Theory Comput. 10:3092–103
    [Google Scholar]
  98. 98. 
    Simm GN, Reiher M 2016. Systematic error estimation for chemical reaction energies. J. Chem. Theory Comput. 12:2762–73
    [Google Scholar]
  99. 99. 
    Husch T, Freitag L, Reiher M 2018. Calculation of ligand dissociation energies in large transition-metal complexes. J. Chem. Theory Comput. 14:2456–68
    [Google Scholar]
  100. 100. 
    Pernot P, Civalleri B, Presti D, Savin A 2015. Prediction uncertainty of density functional approximations for properties of crystals with cubic symmetry. J. Phys. Chem. A 119:5288–304
    [Google Scholar]
  101. 101. 
    Pernot P 2017. The parameter uncertainty inflation fallacy. J. Chem. Phys. 147:104102
    [Google Scholar]
  102. 102. 
    Proppe J, Reiher M 2017. Reliable estimation of prediction uncertainty for physicochemical property models. J. Chem. Theory Comput. 13:3297–317
    [Google Scholar]
  103. 103. 
    Ma Q, Werner HJ 2018. Explicitly correlated local coupled-cluster methods using pair natural orbitals. WIREs Comput. Mol. Sci. 8:e1371
    [Google Scholar]
  104. 104. 
    Stein CJ, Reiher M 2016. Automated selection of active orbital spaces. J. Chem. Theory Comput. 12:1760–71
    [Google Scholar]
  105. 105. 
    Stein CJ, von Burg V, Reiher M 2016. The delicate balance of static and dynamic electron correlation. J. Chem. Theory Comput. 12:3764–73
    [Google Scholar]
  106. 106. 
    Stein CJ, Reiher M 2019. autoCAS: a program for fully automated multiconfigurational calculations. J. Comput. Chem. 40252216–26
  107. 107. 
    Manby FR, Stella M, Goodpaster JD, Miller TF 2012. A simple, exact density-functional-theory embedding scheme. J. Chem. Theory Comput. 8:2564–68
    [Google Scholar]
  108. 108. 
    Tamukong PK, Khait YG, Hoffmann MR 2014. Density differences in embedding theory with external orbital orthogonality. J. Phys. Chem. A 118:9182–200
    [Google Scholar]
  109. 109. 
    Hégely B, Nagy PR, Ferenczy GG, Kállay M 2016. Exact density functional and wave function embedding schemes based on orbital localization. J. Chem. Phys. 145:064107
    [Google Scholar]
  110. 110. 
    Mühlbach AH, Reiher M 2018. Quantum system partitioning at the single-particle level. J. Chem. Phys. 149:184104
    [Google Scholar]
  111. 111. 
    Proppe J, Gugler S, Reiher M 2019. Gaussian process-based refinement of dispersion corrections. J. Chem. Theory Comput. 15116046–60
  112. 112. 
    Haag MP, Vaucher AC, Bosson M, Redon S, Reiher M 2014. Interactive chemical reactivity exploration. ChemPhysChem 15:3301–19
    [Google Scholar]
  113. 113. 
    Haag MP, Reiher M 2014. Studying chemical reactivity in a virtual environment. Faraday Discuss. 169:89–118
    [Google Scholar]
  114. 114. 
    Luehr N, Jin AGB, Martínez TJ 2015. Ab initio interactive molecular dynamics on graphical processing units (GPUs). J. Chem. Theory Comput. 11104536–44
  115. 115. 
    O'Connor M, Deeks HM, Dawn E, Metatla O, Roudaut A et al. 2018. Sampling molecular conformations and dynamics in a multiuser virtual reality framework. Sci. Adv. 4:eaat2731
    [Google Scholar]
  116. 116. 
    Amabilino S, Bratholm LA, Bennie SJ, Vaucher AC, Reiher M, Glowacki DR 2019. Training neural nets to learn reactive potential energy surfaces using interactive quantum chemistry in virtual reality. J. Phys. Chem. A 123:4486–99
    [Google Scholar]
  117. 117. 
    Lu Y 2017. The “OK, Molly” chemistry. Acc. Chem. Res. 50:647–51
    [Google Scholar]
  118. 118. 
    Aspuru-Guzik A, Lindh R, Reiher M 2018. The matter simulation (r)evolution. ACS Cent. Sci. 4:144–52
    [Google Scholar]
  119. 119. 
    Fast E, Chen B, Mendelsohn J, Bassen J, Bernstein M 2018. Iris: a conversational agent for complex tasks. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18) New York: ACM
  120. 120. 
    Lejaeghere K, Bihlmayer G, Björkman T, Blaha P, Blügel S et al. 2016. Reproducibility in density functional theory calculations of solids. Science 351:aad3000
    [Google Scholar]
  121. 121. 
    Glowacki DR, Liang CH, Morley C, Pilling MJ, Robertson SH 2012. MESMER: an open-source master equation solver for multi-energy well reactions. J. Phys. Chem. A 116:9545–60
    [Google Scholar]
  122. 122. 
    Kee R, Miller J, Jefferson T 1980. CHEMKIN: a general-purpose, problem-independent, transportable, FORTRAN chemical kinetics code package. Tech. Rep. SAND80-8003, Sandia Labs
  123. 123. 
    Miller WH 1993. Beyond transition-state theory: a rigorous quantum theory of chemical reaction rates. Acc. Chem. Res. 26:174–81
    [Google Scholar]
  124. 124. 
    Truhlar DG, Garrett BC, Klippenstein SJ 1996. Current status of transition-state theory. J. Phys. Chem. 100:12771–800
    [Google Scholar]
  125. 125. 
    Pollak E, Talkner P 2005. Reaction rate theory: What it was, where is it today, and where is it going?. Chaos 15026116
  126. 126. 
    Garrett BC, Truhlar DG 2005. Variational transition state theory. Theory and Applications of Computational Chemistry CE Dykstra, G Frenking, KS Kim, GE Scuseria67–87 Amsterdam: Elsevier
  127. 127. 
    R. Soc. Chem 2017. Discussion Volume from the Faraday Discussion on Reaction Rate Theory, September 19–21, 2016, Cambridge, UK Cambridge, UK: R. Soc. Chem.
  128. 128. 
    Richardson JO 2018. Perspective: ring-polymer instanton theory. J. Chem. Phys. 148:200901
    [Google Scholar]
  129. 129. 
    Reilly AM, Cooper RI, Adjiman CS, Bhattacharya S, Boese AD et al. 2016. Report on the sixth blind test of organic crystal structure prediction methods. Act. Cryst. B 72:439–59
    [Google Scholar]
  130. 130. 
    Rizzi A, Murkli S, McNeill JN, Yao W, Sullivan M et al. 2018. Overview of the SAMPL6 host–guest binding affinity prediction challenge. J. Comput.-Aided Mol. Des. 32:937–63
    [Google Scholar]
  131. 131. 
    Maeda S, Morokuma K 2012. Toward predicting full catalytic cycle using automatic reaction path search method: a case study on HCo(CO)3-catalyzed hydroformylation. J. Chem. Theory Comput. 8:380–85
    [Google Scholar]
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