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Accelerating rare events and building kinetic Monte Carlo models using temperature programmed molecular dynamics

Published online by Cambridge University Press:  21 December 2017

Abhijit Chatterjee Open the ORCID record for Abhijit Chatterjee [Opens in a new window]
Abhijit Chatterjee*
Affiliation:
Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
*
a)Address all correspondence to this author. e-mail: [email protected]
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Abstract

The temperature programmed molecular dynamics (TPMD) method is a recent addition to the list of rare-event simulation techniques for materials. Study of thermally-activated events that are rare at molecular dynamics (MD) timescales is possible with TPMD by employing a temperature program that raises the temperature in stages to a point where the transitions happen frequently. Analysis of the observed waiting time distribution yields a wealth of information including kinetic mechanisms in the material, their rate constants and Arrhenius parameters. The first part of this review covers the foundations of the TPMD method. Recent applications of TPMD are discussed to highlight its main advantages. These advantages offer the possibility for rapid construction of kinetic Monte Carlo (KMC) models of a chosen accuracy using TPMD. In this regards, the second part focuses on the latest developments on uncertainty measures for KMC models. The third part focuses on current challenges for the TPMD method and ways of resolving them.

Keywords

rare eventssimulationcoarse-grainingkinetics
Type
REVIEW
Information
Journal of Materials Research , Volume 33 , Issue 7: Focus Issue: Advanced Atomistic Algorithms in Materials Science , 13 April 2018 , pp. 835 - 846
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Copyright
Copyright © Materials Research Society 2017 

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Footnotes

Contributing Editor: Enrique Martinez

This section of Journal of Materials Research is reserved for papers that are reviews of literature in a given area.

References

REFERENCES

Bortz, A.B., Kalos, M.H., and Lebowitz, J.L.: A new algorithm for Monte Carlo simulations of Ising spin systems. J. Comput. Phys. 17, 10 (1975).Google Scholar
Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340 (1977).Google Scholar
Fichthorn, K.A. and Weinberg, W.H.: Theoretical foundations of dynamical Monte Carlo simulations. J. Chem. Phys. 95, 1090 (1991).Google Scholar
Chatterjee, A. and Vlachos, D.G.: An overview of spatial microscopic and accelerated kinetic Monte Carlo methods. J. Comput. Mater. Des. 14, 253 (2007).Google Scholar
Haldar, P. and Chatterjee, A.: Seeking kinetic pathways relevant to the structural evolution of metal nanoparticles. Modell. Simul. Mater. Sci. Eng. 23, 25002 (2015).Google Scholar
Haldar, P. and Chatterjee, A.: Connectivity-list based characterization of 3D nanoporous structures formed via selective dissolution. Acta Mater. 127, 379 (2017).Google Scholar
Jaraiz, M., Rubio, E., Castrillo, P., Pelaz, L., Bailon, L., Barbolla, J., Gilmer, G.H., and Rafferty, C.S.: Kinetic Monte Carlo simulations: An accurate bridge between ab initio calculations and standard process experimental data. Mater. Sci. Semicond. Process. 3, 59 (2000).Google Scholar
Bowman, G.R. and Pande, V.S.: Protein folded states are kinetic hubs. Proc. Natl. Acad. Sci. U. S. A. 107, 10890 (2009).Google Scholar
Reuter, K., Frenkel, D., and Scheffler, M.: The steady state of heterogeneous catalysis, studied by first-principles statistical mechanics. Phys. Rev. Lett. 93, 116105 (2004).Google Scholar
Pornprasertsuk, R., Ramanarayanan, P., Musgrave, C.B., and Prinz, F.B.: Predicting ionic conductivity of solid oxide fuel cell electrolyte from first principles. J. Appl. Phys. 98, 103513 (2005).Google Scholar
Yang, Y.G., Johnson, R.A., and Wadley, H.N.G.: Kinetic Monte Carlo simulation of heterometal epitaxial deposition. Surf. Sci. 499, 141 (2002).CrossRefGoogle Scholar
Battaile, C.C. and Srolovitz, D.J.: Kinetic Monte Carlo simulation of chemical vapor deposition. Annu. Rev. Mater. Res. 32, 297 (2002).Google Scholar
Pande, V.S., Beauchamp, K., and Bowman, G.R.: Everything you wanted to know about Markov state models but were afraid to ask. Methods 52, 99 (2010).Google Scholar
Clouet, E., Hin, C., Gendt, D., Nastar, M., and Soisson, F.: Kinetic Monte Carlo simulations of precipitation. Adv. Eng. Mater. 8, 1210 (2006).Google Scholar
Bhoutekar, A., Ghosh, S., Bhattacharya, S., and Chatterjee, A.: A new class of enhanced kinetic sampling methods for building Markov state models. J. Chem. Phys. 147, 152702 (2017).Google Scholar
Bhute, V.J. and Chatterjee, A.: Accuracy of a Markov state model generated by searching for basin escape pathways. J. Chem. Phys. 138, 84103 (2013).Google Scholar
Bhute, V.J. and Chatterjee, A.: Building a kinetic Monte Carlo model with a chosen accuracy. J. Chem. Phys. 138, 244112 (2013).Google Scholar
Allen, M.P. and Tildesley, D.J.: Computer Simulation of Liquids (Oxford Science Publications, Oxford, 1989).Google Scholar
Shaw, D.E., Bowers, K.J., Chow, E., Eastwood, M.P., Ierardi, D.J., Klepeis, J.L., Kuskin, J.S., Larson, R.H., Lindorff-Larsen, K., Maragakis, P., Moraes, M.A., Dror, R.O., Piana, S., Shan, Y., Towles, B., Salmon, J.K., Grossman, J.P., Mackenzie, K.M., Bank, J.A., Young, C., Deneroff, M.M., and Batson, B.: Proceedings of the Conference on High Performance Computing Networking, Storage Analysis—SC’09 (ACM Press, New York, New York, USA, 2009); p. 1.Google Scholar
Laio, A. and Parrinello, M.: Escaping free-energy minima. Proc. Natl. Acad. Sci. U. S. A. 99, 12562 (2002).Google Scholar
Voter, A.F.: Parallel replica method for dynamics of infrequent events. Phys. Rev. B 57, R13985 (1998).Google Scholar
Sorenson, M.R. and Voter, A.F.: Temperature-accelerated dynamics for simulation of infrequent events. J. Chem. Phys. 112, 9599 (2000).Google Scholar
Voter, A.F., Montalenti, F., and Germann, T.C.: Extending the time scales in atomistic simulation of materials. Annu. Rev. Mater. Res. 32, 321 (2002).Google Scholar
Miron, R. and Fichthorn, K.A.: Accelerated molecular dynamics with the bond-boost method. J. Chem. Phys. 119, 6210 (2003).CrossRefGoogle Scholar
Xu, L. and Henkelman, G.: Adaptive kinetic Monte Carlo for first-principles accelerated dynamics. J. Chem. Phys. 129, 114104 (2008).Google Scholar
Kara, A., Trushin, O., Yildirim, H., and Rahman, T.S.: Off-lattice self-learning kinetic Monte Carlo: Application to 2D cluster diffusion on the fcc(111) surface. J. Phys.: Condens. Matter 21, 84213 (2009).Google Scholar
Bolhuis, P.G., Chandler, D., Dellago, C., and Geissler, P.L.: Transition path sampling: Throwing ropes over rough mountain passes, in the dark. Annu. Rev. Phys. Chem. 53, 291 (2002).Google Scholar
Weinan, E., Ren, W., and Vanden-Eijnden, E.: String method for the study of rare events. Phys. Rev. B 66, 52301 (2002).Google Scholar
Henkelman, G., Uberuaga, B.P., and Jónsson, H.: A climbing image nudged elastic band method for finding saddle points and minimum energy paths. J. Chem. Phys. 113, 9901 (2000).Google Scholar
Faradjian, A.K. and Elber, R.: Computing time scales from reaction coordinates by milestoning. J. Chem. Phys. 120, 10880 (2004).Google Scholar
Béland, L.K., Brommer, P., El-Mellouhi, F., Joly, J-F., and Mousseau, N.: Kinetic activation-relaxation technique. Phys. Rev. E 84, 46704 (2011).Google Scholar
Voter, A.F.: Classically exact overlayer dynamics: Diffusion of rhodium clusters on Rh(100). Phys. Rev. B 34, 6819 (1986).CrossRefGoogle ScholarPubMed
Divi, S. and Chatterjee, A.: Accelerating rare events while overcoming the low-barrier problem using a temperature program. J. Chem. Phys. 140, 184115 (2014).CrossRefGoogle ScholarPubMed
Imandi, V. and Chatterjee, A.: Estimating Arrhenius parameters using temperature programmed molecular dynamics. J. Chem. Phys. 145, 34104 (2016).Google Scholar
Wales, D.J.: Energy landscapes: Calculating pathways and rates. Int. Rev. Phys. Chem. 25, 237 (2006).Google Scholar
Chatterjee, A. and Bhattacharya, S.: Uncertainty in a Markov state model with missing states and rates: Application to a room temperature kinetic model obtained using high temperature molecular. J. Chem. Phys. 143, 114109 (2015).CrossRefGoogle Scholar
Ghosh, S., Chatterjee, A., and Bhattacharya, S.: Time-dependent markov state models for single molecule force spectroscopy. J. Chem. Theory Comput. 13, 957 (2017).Google Scholar
Chatterjee, A. and Bhattacharya, S.: Probing the energy landscape of alanine dipeptide and decalanine using temperature as a tunable parameter in molecular dynamics. J. Phys.: Conf. Ser. 759, 12024 (2016).Google Scholar
Haldar, P. and Chatterjee, A.: Nudged-elastic band study of lithium diffusion in bulk silicon in the presence of strain. Energy Procedia 54, 310 (2014).Google Scholar
Jaipal, M. and Chatterjee, A.: Relative occurrence of oxygen-vacancy pairs in yttrium-containing environments of Y2O3-doped ZrO2 can be crucial to ionic conductivity. J. Phys. Chem. C 121, 14534 (2017).Google Scholar
Konwar, D., Bhute, V.J., and Chatterjee, A.: An off-lattice, self-learning kinetic Monte Carlo method using local environments. J. Chem. Phys. 135, 174103 (2011).Google Scholar
Chatterjee, A. and Voter, A.F.: Accurate acceleration of kinetic Monte Carlo simulations through the modification of rate constants. J. Chem. Phys. 132, 194101 (2010).Google Scholar
Van Kampen, N.G.: Stochastic Processes in Physics and Chemistry (North-Holland Personal Library, Amsterdam, the Netherlands, 2007).Google Scholar
Deng, G. and Cahill, L.W.: 1993 IEEE Conference Rec. Nuclear Science Symposium and Medical Imaging Conference (IEEE, San Francisco, California, 1993); pp. 1615–1619.Google Scholar
Bard, A.J. and Faulkner, L.R.: Electrochemical Methods: Fundamentals and Applications (Wiley, New York, 1980).Google Scholar
Vineyard, G.H.: Frequency factors and isotope effects in solid state rate processes. J. Phys. Chem. Solids 3, 121 (1957).Google Scholar
Verma, S., Rehman, T., and Chatterjee, A.: A cluster expansion model for rate constants of surface diffusion processes on Ag, Al, Cu, Ni, Pd, and Pt (100) surfaces. Surf. Sci. 613, 114 (2013).CrossRefGoogle Scholar
Rehman, T., Jaipal, M., and Chatterjee, A.: A cluster expansion model for predicting the activation barrier of atomic processes. J. Comp. Physiol. 243, 244 (2013).Google Scholar