Reaction Space Projector (ReSPer) for Visualizing Dynamic Reaction Routes Based on Reduced-Dimension Space

Springer Science and Business Media LLC - Tập 380 - Trang 1-23 - 2022
Takuro Tsutsumi1, Yuriko Ono2, Tetsuya Taketsugu1,2
1Department of Chemistry, Faculty of Science Hokkaido University, Sapporo, Japan
2Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Japan

Tóm tắt

To analyze chemical reaction dynamics based on a reaction path network, we have developed the “Reaction Space Projector” (ReSPer) method with the aid of the dimensionality reduction method. This program has two functions: the construction of a reduced-dimensionality reaction space from a molecular structure dataset, and the projection of dynamic trajectories into the low-dimensional reaction space. In this paper, we apply ReSPer to isomerization and bifurcation reactions of the Au5 cluster and succeed in analyzing dynamic reaction routes involved in multiple elementary reaction processes, constructing complicated networks (called “closed islands”) of nuclear permutation-inversion (NPI) isomerization reactions, and elucidating dynamic behaviors in bifurcation reactions with reference to bundles of trajectories. Interestingly, in the second application, we find a correspondence between the contribution ratios in the ability to visualize and the symmetry of the morphology of closed islands. In addition, the third application suggests the existence of boundaries that determine the selectivity in bifurcation reactions, which was discussed in the phase space. The ReSPer program is a versatile and robust tool to clarify dynamic reaction mechanisms based on the reduced-dimensionality reaction space without prior knowledge of target reactions.

Tài liệu tham khảo

Fukui K (1970) Formulation of the reaction coordinate. J Phys Chem 74:4161–4163. https://doi.org/10.1021/j100717a029 Schlegel HB (2011) Geometry optimization. Wiley Interdiscip Rev Comput Mol Sci 1:790–809. https://doi.org/10.1002/wcms.34 Ohno K, Maeda S (2004) A scaled hypersphere search method for the topography of reaction pathways on the potential energy surface. Chem Phys Lett 384:277–282. https://doi.org/10.1016/j.cplett.2003.12.030 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 87:1315–1334. https://doi.org/10.1246/bcsj.20140189 Maeda S, Morokuma K (2011) Finding reaction pathways of type A + B → X: toward systematic prediction of reaction mechanisms. J Chem Theory Comput 7:2335–2345. https://doi.org/10.1021/ct200290m Maeda S, Taketsugu T, Morokuma K (2014) Exploring transition state structures for intramolecular pathways by the artificial force induced reaction method. J Comput Chem 35:166–173. https://doi.org/10.1002/jcc.23481 Maeda S, Harabuchi Y (2021) Exploring paths of chemical transformations in molecular and periodic systems: an approach utilizing force. WIREs Comput Mol Sci. https://doi.org/10.1002/wcms.1538 Ebisawa S, Tsutsumi T, Taketsugu T (2021) Geometric analysis of anharmonic downward distortion following paths. J Comput Chem 42:27–39. https://doi.org/10.1002/jcc.26430 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 119:11641–11649. https://doi.org/10.1021/acs.jpca.5b09447 Sumiya Y, Maeda S (2020) Rate constant matrix contraction method for systematic analysis of reaction path networks. Chem Lett 49:553–564. https://doi.org/10.1246/cl.200092 Martínez-Núñez E (2015) An automated method to find transition states using chemical dynamics simulations. J Comput Chem 36:222–234. https://doi.org/10.1002/jcc.23790 Dewyer AL, Argüelles AJ, Zimmerman PM (2018) Methods for exploring reaction space in molecular systems. Wiley Interdiscip Rev Comput Mol Sci 8:1–20. https://doi.org/10.1002/wcms.1354 Mitsuta Y, Shigeta Y (2020) Analytical method using a scaled hypersphere search for high-dimensional metadynamics simulations. J Chem Theory Comput 16:3869–3878. https://doi.org/10.1021/acs.jctc.0c00010 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. https://doi.org/10.1021/acsphyschemau.1c00051 Field-Theodore TE, Taylor PR (2020) ALTRUISM: a higher calling. J Chem Theory Comput 16:4388–4398. https://doi.org/10.1021/acs.jctc.0c00388 Miller WH, Handy NC, Adams JE (1980) Reaction path Hamiltonian for polyatomic molecules. J Chem Phys 72:99–112. https://doi.org/10.1063/1.438959 Kato S, Morokuma K (1980) Potential energy characteristics and energy partitioning in chemical reactions: ab initio MO study of H2CCH2F→H2CCHF+H reaction. J Chem Phys 72:206–217. https://doi.org/10.1063/1.438877 Taketsugu T, Tajima N, Hirao K (1996) Approaches to bifurcating reaction path. J Chem Phys 105:1933–1939. https://doi.org/10.1063/1.472063 Maeda S, Harabuchi Y, Ono Y, Taketsugu T, Morokuma K (2015) Intrinsic reaction coordinate: calculation, bifurcation, and automated search. Int J Quantum Chem 115:258–269. https://doi.org/10.1002/qua.24757 Gordon MS, Chaban G, Taketsugu T (1996) Interfacing electronic structure theory with dynamics. J Phys Chem 100:11512–11525. https://doi.org/10.1021/jp953371o Pratihar S, Ma X, Homayoon Z, Barnes GL, Hase WL (2017) Direct chemical dynamics simulations. J Am Chem Soc 139:3570–3590. https://doi.org/10.1021/jacs.6b12017 Taketsugu T, Gordon MS (1995) Dynamic reaction path analysis based on an intrinsic reaction coordinate. J Chem Phys 103:10042–10049. https://doi.org/10.1063/1.470704 Taketsugu T, Gordon MS (1996) Reaction path Hamiltonian based on a reaction coordinate and a curvature coordinate. J Chem Phys 104:2834–2840. https://doi.org/10.1063/1.471019 Zou W, Sexton T, Kraka E, Freindorf M, Cremer D (2016) A new method for describing the mechanism of a chemical reaction based on the unified reaction valley approach. J Chem Theory Comput 12:650–663. https://doi.org/10.1021/acs.jctc.5b01098 Ito T, Harabuchi Y, Maeda S (2020) AFIR explorations of transition states of extended unsaturated systems: automatic location of ambimodal transition states. Phys Chem Chem Phys 22:13942–13950. https://doi.org/10.1039/D0CP02379E Lee S, Goodman JM (2020) Rapid route-finding for bifurcating organic reactions. J Am Chem Soc 142:9210–9219. https://doi.org/10.1021/jacs.9b13449 Sun L, Song K, Hase WL (2002) A SN2 reaction that avoids its deep potential energy minimum. Science 296:875–878. https://doi.org/10.1126/science.1068053 Hare SR, Li A, Tantillo DJ (2018) Post-transition state bifurcations induce dynamical detours in Pummerer-like reactions. Chem Sci 9:8937–8945. https://doi.org/10.1039/C8SC02653J Campos RB, Tantillo DJ (2019) Designing reactions with post-transition-state bifurcations: asynchronous nitrene insertions into C-C σ bonds. Chem 5:227–236. https://doi.org/10.1016/j.chempr.2018.10.019 Kraka E, Cremer D (2010) Computational analysis of the mechanism of chemical reactions in terms of reaction phases: hidden intermediates and hidden transition states. Acc Chem Res 43:591–601. https://doi.org/10.1021/ar900013p Hong YJ, Tantillo DJ (2014) Biosynthetic consequences of multiple sequential post-transition-state bifurcations. Nat Chem 6:104–111. https://doi.org/10.1038/nchem.1843 Kraka E, Zou W, Tao Y, Freindorf M (2020) Exploring the mechanism of catalysis with the unified reaction valley approach (URVA)—a review. Catalysts 10:691. https://doi.org/10.3390/catal10060691 Jiang B, Li J, Guo H (2016) Potential energy surfaces from high fidelity fitting of ab initio points: the permutation invariant polynomial - neural network approach. Int Rev Phys Chem 35:479–506. https://doi.org/10.1080/0144235X.2016.1200347 Liu Y, Song H, Xie D, Li J, Guo H (2020) Mode specificity in the OH + HO2 → H2O + O2 reaction: enhancement of reactivity by exciting a spectator mode. J Am Chem Soc 142:3331–3335. https://doi.org/10.1021/jacs.9b12467 Lin S, Peng D, Yang W, Gu FL, Lan Z (2021) Theoretical studies on triplet-state driven dissociation of formaldehyde by quasi-classical molecular dynamics simulation on machine-learning potential energy surface. J Chem Phys 155:214105. https://doi.org/10.1063/5.0067176 Olasz B, Czakó G (2019) Uncovering the role of the stationary points in the dynamics of the F − + CH 3 I reaction. Phys Chem Chem Phys 21:1578–1586. https://doi.org/10.1039/C8CP06207B Sharma N, Biswas R, Lourderaj U (2020) Dynamics of a gas-phase SNAr reaction: non-concerted mechanism despite the Meisenheimer complex being a transition state. Phys Chem Chem Phys 22:26562–26567. https://doi.org/10.1039/D0CP05567K Tsutsumi T, Harabuchi Y, Ono Y, Maeda S, Taketsugu T (2018) Analyses of trajectory on-the-fly based on the global reaction route map. Phys Chem Chem Phys 20:1364–1372. https://doi.org/10.1039/C7CP06528K Tsutsumi T, Ono Y, Arai Z, Taketsugu T (2018) Visualization of the intrinsic reaction coordinate and global reaction route map by classical multidimensional scaling. J Chem Theory Comput 14:4263–4270. https://doi.org/10.1021/acs.jctc.8b00176 Tsutsumi T, Ono Y, Arai Z, Taketsugu T (2020) Visualization of the dynamics effect: projection of on-the-fly trajectories to the subspace spanned by the static reaction path network. J Chem Theory Comput 16:4029–4037. https://doi.org/10.1021/acs.jctc.0c00018 Tsutsumi T, Ono Y, Taketsugu T (2021) Visualization of reaction route map and dynamical trajectory in reduced dimension. Chem Commun 57:11734–11750. https://doi.org/10.1039/D1CC04667E Komatsuzaki T, Hoshino K, Matsunaga Y, Rylance GJ, Johnston RL, Wales DJ (2005) How many dimensions are required to approximate the potential energy landscape of a model protein? J Chem Phys 122:084714. https://doi.org/10.1063/1.1854123 Hare SR, Bratholm LA, Glowacki DR, Carpenter BK (2019) Low dimensional representations along intrinsic reaction coordinates and molecular dynamics trajectories using interatomic distance matrices. Chem Sci 10:9954–9968. https://doi.org/10.1039/C9SC02742D Peng J, Xie Y, Hu D, Lan Z (2021) Analysis of bath motion in MM-SQC dynamics via dimensionality reduction approach: principal component analysis. J Chem Phys 154:094122. https://doi.org/10.1063/5.0039743 Rashmi R, Yadav K, Lourderaj U, Paranjothy M (2021) Second-order saddle dynamics in isomerization reaction. Regul Chaotic Dyn 26:119–130. https://doi.org/10.1134/S1560354721020027 Casier B, Carniato S, Miteva T, Capron N, Sisourat N (2020) Using principal component analysis for neural network high-dimensional potential energy surface. J Chem Phys 152:234103. https://doi.org/10.1063/5.0009264 Pisani P, Caporuscio F, Carlino L, Rastelli G (2016) Molecular dynamics simulations and classical multidimensional scaling unveil new metastable states in the conformational landscape of CDK2. PLoS One 11:e0154066. https://doi.org/10.1371/journal.pone.0154066 Li X, Xie Y, Hu D, Lan Z (2017) Analysis of the geometrical evolution in on-the-fly surface-hopping nonadiabatic dynamics with machine learning dimensionality reduction approaches: classical multidimensional scaling and isometric feature mapping. J Chem Theory Comput 13:4611–4623. https://doi.org/10.1021/acs.jctc.7b00394 Oliveira AB, Yang H, Whitford PC, Leite VBPP (2019) Distinguishing biomolecular pathways and metastable states. J Chem Theory Comput 15:6482–6490. https://doi.org/10.1021/acs.jctc.9b00704 Shi W, Jia T, Li A (2020) Quasi-classical trajectory analysis with isometric feature mapping and locally linear embedding: deep insights into the multichannel reaction on an NH3+ (4A) potential energy surface. Phys Chem Chem Phys 22:17460–17471. https://doi.org/10.1039/D0CP01941K Evans DA, Wales DJ (2003) Free energy landscapes of model peptides and proteins. J Chem Phys 118:3891–3897. https://doi.org/10.1063/1.1540099 Becker OM, Karplus M (1997) The topology of multidimensional potential energy surfaces: theory and application to peptide structure and kinetics. J Chem Phys 106:1495–1517. https://doi.org/10.1063/1.473299 Torgerson WS (1952) Multidimensional scaling: I. Theory and method. Psychometrika 17:401–419. https://doi.org/10.1007/BF02288916 Härdle WK, Simar L (2015) Applied Multivariate Statistical Analysis, 3rd edn. Springer, Berlin Trosset MW, Priebe CE (2008) The out-of-sample problem for classical multidimensional scaling. Comput Stat Data Anal 52:4635–4642. https://doi.org/10.1016/j.csda.2008.02.031 Rogers DJ, Tanimoto TT (1960) A computer program for classifying plants. Science 132:1115–1118. https://doi.org/10.1126/science.132.3434.1115 Fukutani T, Miyazawa K, Iwata S, Satoh H (2021) G-RMSD: root mean square deviation based method for three-dimensional molecular similarity determination. Bull Chem Soc Jpn 94:655–665. https://doi.org/10.1246/bcsj.20200258 Kabsch W (1976) A solution for the best rotation to relate two sets of vectors. Acta Crystallogr Sect A 32:922–923. https://doi.org/10.1107/S0567739476001873 GitHub Calculate Root-mean-square deviation (RMSD) of two molecules using rotation. In: GitHub. http://github.com/charnley/rmsd Borg I, Groenen P (2005) Modern Multidimensional Scaling: Theory and Applications. Springer Series in Statistics. Springer, Berlin Young G, Householder AS (1938) Discussion of a set of points in terms of their mutual distances. Psychometrika 3:19–22. https://doi.org/10.1007/BF02287916 Haruta M (2005) Gold rush. Nature 437:1098–1099. https://doi.org/10.1038/4371098a Gao M, Lyalin A, Takagi M, Maeda S, Taketsugu T (2015) Reactivity of gold clusters in the regime of structural fluxionality. J Phys Chem C 119:11120–11130. https://doi.org/10.1021/jp511913t Muramatsu S, Koyasu K, Tsukuda T (2018) Abstraction of the I atom from CH3I by gas-phase Aun- (n = 1–4) via reductive activation of the C-I bond. ACS Omega 3:16874–16881. https://doi.org/10.1021/acsomega.8b02809 Sugiuchi M, Maeba J, Okubo N, Iwamura M, Nozaki K, Konishi K (2017) Aggregation-induced fluorescence-to-phosphorescence switching of molecular gold clusters. J Am Chem Soc 139:17731–17734. https://doi.org/10.1021/jacs.7b10201 Miyamoto M, Taketsugu T, Iwasa T (2021) A comparative study of structural, electronic, and optical properties of thiolated gold clusters with icosahedral vs face-centered cubic cores. J Chem Phys 155:094304. https://doi.org/10.1063/5.0057566 Harabuchi Y, Ono Y, Maeda S, Taketsugu T (2015) Analyses of bifurcation of reaction pathways on a global reaction route map: a case study of gold cluster Au5. J Chem Phys 143:014301. https://doi.org/10.1063/1.4923163 Perdew JP, Burke K, Ernzerhof M (1996) Generalized gradient approximation made simple. Phys Rev Lett 77:3865–3868. https://doi.org/10.1103/PhysRevLett.77.3865 Perdew JP, Burke K, Ernzerhof M (1997) Generalized gradient approximation made simple [Phys. Rev. Lett. 77, 3865 (1996)]. Phys Rev Lett 78:1396–1396. https://doi.org/10.1103/PhysRevLett.78.1396 Hay PJ, Wadt WR (1985) Ab initio effective core potentials for molecular calculations. Potentials for K to Au including the outermost core orbitale. J Chem Phys 82:299–310. https://doi.org/10.1063/1.448975 Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich A, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz J V, Izmaylov AF, Sonnenberg JL, Williams-Young D, Ding F, Lipparini F, Egidi F, Goings J, Peng B, Petrone A, Henderson T, Ranasinghe D, Zakrzewski VG, Gao J, Rega N, Zheng G, Liang W, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Throssell K, Montgomery, J. A. J, Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Keith T, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Millam JM, Klene M, Adamo C, Cammi R, Ochterski JW, Martin RL, Morokuma K, Farkas O, Foresman JB, Fox DJ (2013) Gaussian 09. Gaussian, Inc., Wallingford CT Maeda S, Osada Y, Morokuma K, Ohno K (2011) GRRM11, version 11.01. Harabuchi Y, Okai M, Yamamoto R, Tsutsumi T, Ono Y, Taketsugu T (2020) SPPR (a developmental version). Hokkaido University: Sapporo, Japan Taketsugu T, Wales DJ (2002) Theoretical study of rearrangements in water dimer and trimer. Mol Phys 100:2793–2806. https://doi.org/10.1080/00268970210142648 Rawlinson JI, Fábri C, Császár AG (2021) Exactly solvable 1D model explains the low-energy vibrational level structure of protonated methane. Chem Commun 57:4827–4830. https://doi.org/10.1039/D1CC01214B Williams DMG, Eisfeld W (2020) Complete nuclear permutation inversion invariant artificial neural network (CNPI-ANN) diabatization for the accurate treatment of vibronic coupling problems. J Phys Chem A 124:7608–7621. https://doi.org/10.1021/acs.jpca.0c05991 Thomas JB, Waas JR, Harmata M, Singleton DA (2008) Control elements in dynamically determined selectivity on a bifurcating surface. J Am Chem Soc 130:14544–14555. https://doi.org/10.1021/ja802577v Agaoglou M, García-Garrido VJ, Katsanikas M, Wiggins S (2020) The phase space mechanism for selectivity in a symmetric potential energy surface with a post-transition-state bifurcation. Chem Phys Lett 754:137610. https://doi.org/10.1016/j.cplett.2020.137610