Avoiding traps in trajectory space: Metadynamics enhanced transition path sampling
Tóm tắt
We propose a transition path sampling (TPS) scheme designed to enhance sampling in systems with multiple reaction channels. In this method, based on a combination of the metadynamics algorithm with the TPS shooting move, a history dependent bias drives the simulation towards unexplored reaction channels. The bias, constructed as a superposition of repulsive Gaussian potentials deposited on the trajectories harvested in the course of the simulation, acts only during the initial stage of the trajectory generation, but leaves the dynamics along the trajectories unaffected such that the sampled pathways are true dynamical trajectories. Simulations carried out for two test systems indicate that the new approach effortlessly switches between distinct reaction channels even if they are separated by high barriers in trajectory space.
Tài liệu tham khảo
A.K. Faradjian, R. Elber, J. Chem. Phys. 120, 10880 (2004)
R.J. Allen, P.B. Warren, P.R. ten Wolde, Phys. Rev. Lett. 94, 018104 (2005)
W.E.W. Ren, E. Vanden-Eijnden, J. Phys. Chem. B 109, 6688 (2005)
P.G. Bolhuis, D. Chandler, C. Dellago, Annu. Rev. Phys. Chem. 53, 291 (2002)
C. Dellago, P.G. Bolhuis, F.S. Csajka, D. Chandler, J. Chem. Phys. 108, 1964 (1998)
C. Dellago, P.G. Bolhuis, P.L. Geissler, D. Chandler, Adv. Chem. Phys. 123, 1 (2002)
T.S. van Erp, P.G. Bolhuis, J. Comput. Chem. 205, 157 (2005)
D. Moroni, T.S. van Erp, P.G. Bolhuis, Phys. A 340, 395 (2004)
P.G. Bolhuis, C. Dellago, Eur. Phys. J. Special Topics 224, 1 (2015)
C. Dellago, P.G. Bolhuis, in Advanced Computer Simulation Approaches for Soft Matter Sciences III (Springer, New York, 2009), vol. 221 of Advances in Polymer Science, p. 167
E. Marinari, G. Parisi, Europhys. Lett. 19, 451 (1992)
P.G. Bolhuis, J. Chem. Phys. 129, 114108 (2008)
T.J.H. Vlugt, B. Smit, Phys. Chem. Comm. 4, 11 (2001)
T.S. van Erp, Phys. Rev. Lett. 98, 268301(2007)
T.S. van Erp, Comp. Phys. Comm. 179, 34 (2008)
D.W.H. Swenson, P.G. Bolhuis, J. Chem. Phys. 141, 044101 (2014)
F. Wang, D.P. Landau, Phys. Rev. Lett. 86, 2050 (2001)
E.E. Borrero, C. Dellago, J. Chem. Phys. 133, 134112 (2010)
T. Huber, A.E. Torda, W.F. Gunsteren, J. Comput. Aided. Mol. Des. 8, 695 (1994)
A. Laio, M. Parrinello, Proc. Natl. Acad. Sci. USA 99, 12562 (2002)
A. Laio, F.L. Gervasio, Rep. Prog. Phys. 71, 126601 (2008)
G. Bussi, A. Laio, M. Parrinello, Phys. Rev. Lett. 96, 090601 (2006)
P. Tiwary, M. Parrinello, Phys. Rev. Lett. 111, 230602 (2013)
C. Dellago, P.G. Bolhuis, D. Chandler, J. Chem. Phys. 108, 9236 (1998)
D. Provansi, A. Bortolato, M. Filizola, Biochem. 48, 10020 (2009)
D. Branduardi, F.L. Gervasio, M. Parrinello, J. Chem. Phys. 126, 054103 (2007)
J.D. Weeks, D. Chandler, H.C. Andersen, J. Chem. Phys. 54, 5237 (1971)
D. Frenkel, B. Smit, Understanding Molecular Simulation: From Algorithms to Applications (Academic, San Diego, CA, 2002)
M. Chopra, R. Malshe, A.S. Reddy, J.J. de Pablo, J. Chem. Phys. 128, 144104 (2008)
E.E. Borrero, F. Escobedo, J. Phys. Chem. B 113, 6434 (2009)
M. Grünwald, C. Dellago, Nano Lett. 9, 2099 (2009)
M. Grünwald, K. Lutker, A.P. Alivisatos, E. Rabani, P.L. Geissler, Nano Lett. 13, 1367 (2013)