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K-computer Simulation for Electrochemical Energy Conversion

Sugino Group

Energy flows through matters in various forms and controlling the energy flow is an important subject of interdisciplinary material research. The energy flow dynamics is complex but is particularly rich near the interface, where the energy carrier (particle or field) switches from one to another. The electrochemical interface offers a field for converting an ionic flow to the electronic flow, and this conversion process is used as the principle of a battery. The corresponding basic process is the electron-transfer reaction dynamics, which has now attracted renewed attention not only from electrochemistry but also from many other fields. In this context, this group is intended to describe the dynamics, with the help of the K-computer power, unambiguously within the first-principles molecular dynamics (FPMD) scheme. A simulation team was organized from research groups of Osaka/Tohoku/Nagoya University and national laboratories (AIST and NIMS). The joint simulation team has recently made progress toward the goal.

Fig. 1. The ESM modeling of the electrochemical interface. The model consists of the electrode slab (Pt(111) in the present case), solution slab (liquid water plus a hydronium ion) and the dielectric continuum. The continuum is characterized by the dielectric constant. The bias potential is controlled with the potentiostat scheme [2].

The team modeled the system using a metal slab, a solution slab, and the dielectric continuum slab as shown in Fig. 1. This model, called the effective screening medium (ESM) model, was originally developed by the present group in 2006 [1] but was recently improved to enable a precise control of the bias potential [2,3]. By this technique one can describe a subtle imbalance of chemical equilibrium between the electrons and the ions, essential for studying an electrochemical process. This algorithm was implemented to an FPMD code, called STATE-senri, which had been highly parallelized for K-computer.

One of the first target of this new FPMD scheme was to investigate how the solvent fluctuation would affect the catalytic activity. The solvent fluctuation causes the bias potential to fluctuate, whereby assisting the catalytic reaction to occur. The fluctuation is small for the bulk catalysts but is increasingly enhanced as the catalyst is reduced in size. By combining the simulation with the classical Marcus theory, the exchange current for the reaction, which is a measure of catalytic activity, was found to be enhance by 15 when the size of nano-particle is reduced to 3 nm in diameter [4]. This enhancement factor is indeed a large value which is comparable to (or larger than) that achievable with the nano-shell method, i.e., a technique to enhance the activity by alloying the subsurface Pt. The fluctuation effect has not been seriously studied so far, but the present calculation suggests further room for enhancing the catalytic activity by controlling the structure of the interface.

This group has been performing a large number of first-principles simulations, with or without the ESM, and the results have accumulated. With those data, this group has reconsidered how the reduction of an oxygen molecule would occur, which is the central issue of electrochemistry and many efforts have been devoted. When the simulation results were analyzed together with recently available experimental results, it was found that some of the reaction pathways can be excluded and accordingly narrowed down the possibilities [5]. It was then concluded that the reaction proceeds mainly through a pathway called the associative pathway in the steady state, while the other one, called the dissociative pathway, is chosen prior to steady state. This analysis emphasizes importance of the dynamical effect in discussing the dominant mechanism.

More extensive simulations are now conducted in K-computer. The team is preparing further for the coming exa-flops supercomputers to establish a microscopic theory of electrochemical energy conversion.


References
  • [1] M. Otani and O. Sugino, Phys. Rev. B 73, 115407 (2006).
  • [2] N. Bonnet, T. Morishita, O. Sugino, and M. Otani, Phys. Rev. Lett. 109, 266101 (2012).
  • [3] I. Hamada, O. Sugino, N. Bonnet, and M. Otani, Phys. Rev. B 88, 155427 (2013).
  • [4] N. Bonnet, O. Sugino, and M. Otani, J. Chem. Phys. 140, 044703 (2014).
  • [5] N. Bonnet, O. Sugino, and M. Otani, to be published in J. Phys. Chem. C.
Authors
  • O. Sugino, N. Bonnet, I. Hamadaa, M. Otania, T. Ikeshojia, Y. Morikawab, K. Inagakib, H. Kizakib, K. Akagic, M. Araidaid
  • aAdvanced Industrial Science and Technology
  • bOsaka University
  • cTohoku University
  • dNagoya University