Author(s): Giovanni Maria Piccini
Publication: Bunsenmagazin, Issue 1 2019, Aspekte, Seiten: 04 - 12
Publisher: Deutsche Bunsen-Gesellschaft für physikalische Chemie e.V., Frankfurt
Experimental, theoretical and computational investigations of reaction mechanisms represent one of the most challenging fields in chemistry. A deep understanding of activated processes allows chemists to comprehend the nature of chemical transformations and helps designing molecules in order to govern the reaction. Besides the fundamental academic interest, the study of chemical reactions has a great impact in everyday life.
Understanding a chemical reaction is not simple. To properly investigate these events one must understand how and why a reaction proceeds from reactants to products with a specific mechanism, and how fast this process is. Changes in the molecular structure, ambient conditions, different solvation environments, allow chemist to interfere with the chemical transformation process in order to achieve the desired products. Therefore, the atomistic details of the reaction mechanism are fundamental to reach this goal.
Quantum chemistry (QC) has been very successful in unravelling the nature of many chemical reactions at the molecular level. Several computational techniques and new physical chemistry concepts have been developed bridging the computational results to the experimental findings. Reaction paths and coordinates, reactive trajectories, potential energy minima and saddle points have become customary objects in computational chemistry. However, their application is extremely limited if not meaningless to systems for which the structural complexity and fluxionality is very high, e.g. solvated systems.
A valid alternative is represented byab initioMolecular dynamics (MD). MD simulations represent a powerful tool for studying a vast range of chemical systems. Many equilibrium properties can be calculated and their related atomistic details investigated. Since decades these techniques have been applied to several phenomena in different kind of systems from materials to biological macromolecules. Unfortunately, many important processes occur on a time scale that is larger by orders of magnitude than the one accessible by standard MD simulations. These phenomena are commonly referred to as rare events.
To circumvent this problem several methods and computational techniques have been proposed in order to enhance the sampling of these events. This is achieved by accelerating the transitions between metastable states, thus, broadening the probability distribution between them. Because of the direct relationship between probability distribution and free energy, these methods are often referred as free energy methods. A large family of these techniques is based on the concept of the collective variable (CV), a parameter that characterizes and distinguishes the metastable states of interest. Among all, a very popular and widely used method to enhance the sampling between free energy minima is Metadynamics (MetaD).[11, 12] This approach makes use of an adaptive and history dependent bias potential that enhances the fluctuations within the free energy basins, thus, favouring transitions. MetaD allows the exploration of the configurational space, the reconstruction of the free energy surface (FES), and the determination of the kinetics of the process.[13, 14] In spite of its popularity and vast use in several application, MetaD has been much less frequently applied to chemical reactivity. Among all the 3000 and more citations of the landmark paper by Laio and Parrinello “Escaping free energy minima” introducing the MetaD algorithm, only about 80 papers report its application to chemical reactions. The research presented in the present report aims at developing new methods and approaches for studying chemical reactions within the framework of Metadynamics. Based on the previous works and on recent promising developments in the field, together with the development of faster computers and efficient algorithms, MetaD could potentially become a routine method in the study of chemical reactions in complex systems competing with the aforementioned well established methods. Applications to relevant problems in chemistry will be tackled. They range from homogeneous and heterogeneous catalysis up to the role of water in chemistry such as acid/base reactions and anticancer drugs activity. The overall and most ambitious goal of this project is to move forward the frontiers in quantum chemical simulations.
Cite this: Giovanni Maria Piccini (2019): Removing Barriers to understand Chemistry. Bunsenmagazin 2019, 1: 4-12. Frankfurt am Main: Deutsche Bunsen-Gesellschaft für physikalische Chemie e.V. DOI: 10.26125/63fj-f802
 Truhlar, D. G.; Steckler, R.; Gordon, M. S. Chem. Rev. 1987, 87, 217–236.
 Friedrich, B.; Herman, Z.; Zahradnik, R.; Havlas, Z. Adv. Quant. Chem. 1988, 19, 257–288.
 Gonzalez, C.; S chlegel, H. B. J Chem. Phys 1989, 90, 2154–2161.
 Fukui, K. J. Phys. Chem. 1970, 74, 4161–4163.
 POLANYI, J. C. Acc. Chem. Res. 1972, 5, 161–&.
 Schlegel, H. B. J. Comput. Chem. 2003, 24, 1514–1527.
 Friedrich, B.; Herman, Z.; Zahradnik, R.; Havlas, Z. Adv. Quant. Chem.; Elsevier, 1988; Vol. 19; pp 257–288.
 Marx, D.; Urg, J. Quantum 2000, 1–149.
 Abrams, C.; Bussi, G. Entropy 2014, 16, 163–199.
 Hartmann, C.; Latorre, J. C.; Ciccotti, G. Eur. Phys. J. Spec. Top. 2011, 200, 73–89.
 Laio, A.; Parrinello, M. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 12562–12566.
 Barducci, A.; Bussi, G.; Parrinello, M. Phys. Rev. Lett. 2008, 100.
 Barducci, A.; Bonomi, M.; Parrinello, M. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2011, 1, 826–843.
 Tiwary, P.; Parrinello, M. Phys. Rev. Lett. 2013, 111, 230602.
 Zheng, S.; Pfaendtner, J. Mol. Simul. 2015, 41, 55–72.
 Cramer, C. J. 2nd John Wiley Sons 2004, 550–555.
 Simons, J.; Joergensen, P.; Taylor, H.; Ozment, J. J. Phys. Chem. 1983, 87, 2745–2753.
 Schlegel, H. B. Mod. Electron. Struct. Theory Part I; World Scientifi c, 1995; pp 459–500.
 Baker, J. J. Comput. Chem. 1986, 7, 385.
 Peng, C.; Ayala, P. Y.; Schlegel, H. B.; Frisch, M. J. J. Comp. Chem. 1996, 17, 49.
 Billeter, S. R.; Turner, A. J.; Thiel, W. Phys. Chem. Chem. Phys. 2000, 2, 2177–2186.
 Govind, N.; Petersen, M.; Fitzgerald, G.; King-Smith, D.; Andzelm, J. Comput. Mater. Sci. 2003, 28, 250–258.
 Bonomi, M.; Barducci, A.; Parrinello, M. J. Comput. Chem. 2009, 30, 1615–1621.
 Tiwary, P.; Parrinello, M. J. Phys. Chem. B 2015, 119, 736–742.
 Ensing, B.; De Vivo, M.; Liu, Z.; Moore, P.; Klein, M. L. Acc. Chem. Res. 2006, 39, 73–81.
 Fu, C. D.; Oliveira, L. F. L.; Pfaendtner, J. J. Chem. Theory Comput. 2017, 13, 968–973.
 Fisher, R. A. Ann. Eugen. 1936, 7, 179–188.
 Mendels, D.; Piccini, G.; Parrinello, M. J. Phys. Chem. Lett. 0, 2776–2781.
 Piccini, G.; Po lino, D.; Parrinello, M. J. Phys. Chem. Lett. 2017, 8, 4197–4200.
 Piccini, G.; Mendels, D.; Parrinello, M. J. Chem. Theory Comput. 2018, 14, 5040–5044, PMID: 30222350.
 Fleming, K. L.; Tiwary, P.; Pfaendtner, J. J. Phys. Chem. A 2016, 120, 299–305.
 Piccini, G.; McCarty, J. J.; Valsson, O.; Parrinello, M. J. Phys. Chem. Lett. 2017, 8, 580–583.
 Vande Linde, S. R.; Hase, W. L. J. Chem. Phys. 1990, 93, 7962–7980.
 Tucker, S. C.; Truhlar, D. G. J. Phys. Chem. 1989, 93, 8138–8142.
 Branduardi, D.; Faraldo-Gomeź, J. D. J. Chem. Theory Comput. 2013, 9, 4140–4154.
 Ensing, B.; Laio, A.; Gervasio, F. L.; Parrinello, M.; Klein, M. L. J. Am. Chem. Soc. 2004, 126, 9492–9493.
 Dougherty, R. C.; Dalton, J.; David roberts, J. Org. Mass Spectrom. 1974, 8, 77–79.
 Schlegel, H. B.; Mislow, K.; Bernardi, F.; Bottoni, A. Theor. Chim. Acta 1977, 44, 245–256.
 Szabó, I.; Czakó, G. Nat. Commun. 2015, 6.
 Xie, J.; Hase, W. L. Science (80-. ). 2016, 352, 32–33.
 Sæthre, L. J.; Thomas, T. D.; Svensson, S. J. Chem. Soc. Perkin Trans. 2 1997, 749–756.
 Aizman, A.; Contreras, R.; Galván, M.; Cedillo, A.; Santos, J. C.; Chamorro, E. J. Phys. Chem. A 2002, 106, 7844–7849.
 Yang, Z.; Ding, Y.; Zhao, D. ChemPhysChem 2008, 9, 2379–2389.
 Franzen, S.; Cochran, K. H.; Weng, J.; Bartolotti, L.; Delley, B. Chem. Phys. 2016, 464, 46–54.
 Thiel, W. Angew. Chem. Int. Ed. 2014, 53, 8605–8613.
 Kaminsky, W.; Sinn, H. Zeitschrift für Phys. Chemie; Springer Science & Business Media, 1988; Vol. 115; p 442.
 Spaleck, W.; Antberg, M.; Dolle, V.; Klein, R.; Rohrmann, J.; Winter, A. New J. Chem. 1990, 14, 499–503.
 Brintzinger, H. H.; Fischer, D.; Mulhaupt,¨ R.; Rieger, B.; Waymouth, R. M. Angew. Chemie Int. Ed. English 1995, 34, 1143.
 Motta, A.; Fragalà, I. L.; Marks, T. J. J. Chem. Theory Comput. 2013, 9, 3491–3497.
 Alongi, K. S.; Shields, G. C. Annu. Rep. Comput. Chem.; Elsevier, 2010; Vol. 6; pp 113–138.
 Tummanapelli, A. K.; Vasudevan, S. J. Phys. Chem. B 2014, 118, 13651–13657.
 de Alba Ortiz, A.; Tiwari, A.; Puthenkalathil, R. C.; Ensing, B. J. Chem. Phys. 2018, 149, 72320.
 Park, J. M.; La io, A.; Iannuzzi, M.; Parrinello, M. J. Am. Che. Soc. 2006, 128, 11318–11319.
 Grifoni, E.; Piccini, G.; Parrinello, M. Artic. Prep.
 Tuñoń, I.; Silla, E.; Ruiz-Lopeź, M. F. Chem. Phys. Lett. 2000, 321, 433–437.
 Campo, M. G. J. Chem. Phys. 2006, 151, 1–8.
 Lo, C.; Trout, B. J. Catal. 2004, 227, 77–89.
 Derouane, E. G.; Vedrine, J. C.; Pinto, R. R.; Borges, P. M.; Costa, L.; Lemos, M.; Lemos, F.; Ribeiro, F. R. Catal. Rev. 2013, 55, 454–515.
 Perdew., J. P.; Burken, K.; Ernzerhof, M. Phys. Rev. Lett. 1996, 77, 3865–3868.
 Grimme, S. J Comput Chem 2006, 27, 1787–1799.
 Cheng, Q.; Shi, H.; Wang, H.; Min, Y.; Wang, J.; Liu, Y. Chem. Comm. 2014, 50, 7427–7430.
 Pathak, R. K.; Marrache, S.; Choi, J. H.; Berding, T. B.; Dhar, S. Angew. Chem. Int. Ed. 2014, 53, 1963–1967.
 Ponte, F.; Russo, N.; Sicilia, E. Chem. Eur. J. 2018, 24, 9572– 9580.
Download the full article