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

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Accelerating rare events and building kinetic Monte Carlo models using temperature programmed molecular dynamics Abhijit Chatterjeea) Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India (Received 4 September 2017; accepted 20 November 2017)

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.


Recent years have witnessed several attempts to build kinetic Monte Carlo (KMC) models1–4 of materials that are able to reveal fundamentally novel and unique information about the structure, dynamics, and interactions within these materials over long-time scales.5–14 An essential step toward building a realistic KMC model of a material involves gaining knowledge of atomisticscale events that are crucial to the dynamics. We term such events as dynamically relevant events.15–17 One strategy that can be used to accomplish this step entails the use of molecular dynamics (MD) method,18 which has gained significant popularity because of its simplicity. Most research groups around the world possess computational resources that allow for a microsecond long MD simulations of nano-sized systems.19 It becomes challenging to study the dynamically-relevant events that are rare at the accessible MD timescales. Significant strides have been made in the algorithmic development of rare-event techniques that can overcome these bottlenecks.20–31 Yet several questions remain about the relevance of mechanisms found using these techniques, and whether the complexities arising due to the large number of configurations and kinetic pathways while building KMC models can be handled by the rareevent techniques.

Contributing Editor: Enrique Martinez a) Address all correspondence to this author. e-mail: [email protected] DOI: 10.1557/jmr.2017.460

It will be greatly beneficial if the strengths of the MD method, namely, providing exact rate constants32 and sampling of dynamically-relevant events without any prior assumptions about these events,15–17 can be retained as far as possible while addressing the computational cost issue.

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