ABSTRACT
The classical theory of the electrical double layer (EDL) does not consider the effects of the electrode surface structure on the EDL properties. Moreover, the best agreement between the traditional EDL theory and experiments has been achieved so far only for a very limited number of ideal systems, such as liquid metal mercury electrodes, for which it is challenging to operate with specific surface structures. In the case of solid electrodes, the predictive power of classical theory is often not acceptable for electrochemical energy applications, e.g., in supercapacitors, due to the effects of surface structure, electrode composition, and complex electrolyte contributions. In this work, we combine ab initio molecular dynamics (AIMD) simulations and electrochemical experiments to elucidate the relationship between the structure of Pt(hkl) surfaces and the double-layer capacitance as a key property of the EDL. Flat, stepped, and kinked Pt single crystal facets in contact with acidic HClO4 media are selected as our model systems. We demonstrate that introducing specific defects, such as steps, can substantially reduce the EDL capacitances close to the potential of zero charge (PZC). Our AIMD simulations reveal that different Pt facets are characterized by different net orientations of the water dipole moment at the interface. That allows us to rationalize the experimentally measured (inverse) volcano-shaped capacitance as a function of the surface step density.
ABSTRACT
Subsystem density functional theory (DFT) is emerging as a powerful electronic structure method for large-scale simulations of molecular condensed phases and interfaces. Key to its computational efficiency is the use of approximate nonadditive noninteracting kinetic energy functionals. Unfortunately, currently available nonadditive functionals lead to inaccurate results when the subsystems interact strongly such as when they engage in chemical reactions. This work disrupts the status quo by devising a workflow that extends subsystem DFT's applicability also to strongly interacting subsystems. This is achieved by implementing a fully automated adaptive definition of subsystems which is realized during geometry optimizations or ab initio molecular dynamics simulations. The new method prescribes subsystem merging and splitting events redistributing the resources (both for work and data) in an efficient way making use of modern parallelization strategies and object-oriented programming. We showcase the method with examples probing from moderate-to-strong inter-subsystem interactions, opening the door to using subsystem DFT for modeling chemical reactions in molecular condensed phases with a black box computational tool.
Subject(s)
Molecular Dynamics Simulation , Quantum Theory , Density Functional TheoryABSTRACT
In this study, we perform a systematic search to find the possible lowest energy structure of silicon nanoclusters Sin (n = 8-80) by means of an evolutionary algorithm. The fitness function for this search is the total energy of density functional tight binding (DFTB). To be on firm ground, we take several low energy structures of DFTB and perform further geometrical optimization by density functional theory (DFT). Then we choose structures with the lowest DFT total energy and compare them with the reported lowest energy structures in the literature. In our search, we found several lowest energy structures that were previously unreported. We further observe a geometrical transition at n = 27 from elongated to globular structures. In addition, the optical gap of the lowest energy structures is investigated by time-dependent DFTB (TD-DFTB) and time-dependent DFT (TD-DFT). The results show the same trend in TD-DFTB and TD-DFT for the optical gap. We also find a sudden drop in the optical gap at n = 27, precisely where the geometrical transition occurs.
