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A major challenge in the molecular simulation of electric double layer capacitors (EDLCs) is the choice of an appropriate model for the electrode. Typically, in such simulations the electrode surface is modeled using a uniform fixed charge on each of the electrode atoms, which ignores the electrode response to local charge fluctuations induced by charge fluctuations in the electrolyte. In this work, we evaluate and compare this Fixed Charge Method (FCM) with the more realistic Constant Potential Method (CPM), [Reed, et al., J. Chem. Phys., 126, 084704 (2007)], in which the electrode charges fluctuate in order to maintain constant electric potential in each electrode. For this comparison, we utilize a simplified LiClO$_4$-acetonitrile/graphite EDLC. At low potential difference ($DeltaPsile 2V$), the two methods yield essentially identical results for ion and solvent density profiles; however, significant differences appear at higher $DeltaPsi$. At $DeltaPsige 4V$, the CPM ion density profiles show significant enhancement (over FCM) of partially electrode solvated Li$^+$ ions very close to the electrode surface. The ability of the CPM electrode to respond to local charge fluctuations in the electrolyte is seen to significantly lower the energy (and barrier) for the approach of Li$^+$ ions to the electrode surface.
We induce surface carrier densities up to $sim7cdot 10^{14}$cm$^{-2}$ in few-layer graphene devices by electric double layer gating with a polymeric electrolyte. In 3-, 4- and 5-layer graphene below 20-30K we observe a logarithmic upturn of resistanc
We propose a new model for treating solid-phase photoprocesses in interstellar ice analogues. In this approach, photoionization and photoexcitation are included in more detail, and the production of electronically-excited (suprathermal) species is ex
It has been a challenge to accurately simulate Li-ion diffusion processes in battery materials at room temperature using {it ab initio} molecular dynamics (AIMD) due to its high computational cost. This situation has changed drastically in recent yea
The predominance of Kohn-Sham density functional theory (KS-DFT) for the theoretical treatment of large experimentally relevant systems in molecular chemistry and materials science relies primarily on the existence of efficient software implementatio
Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develo