## Atomistic Simulations of Energy Materials

Atomistic modeling and simulations have established itself as a useful tool for understanding and designing electrolyte materials. In this type of modeling, computational methods are used to simulate material properties and chemical processes on the atomic/molecular level. Consequently, one can obtain information on the fundamental properties of materials such as formation enthalpies, elastic constants, band structures, and ionic conductivity. Although atomistic modeling was usually used to complement experiments and to provide atomic-level mechanistic insights, atomistic modeling is now used in computer-assisted materials design. Commonly used atomistic simulation methods include first-principles calculations (e.g., density functional theory (DFT)), and MD (e.g., classical and ab initio MD simulations (AIMD)). DFT and AIMD can be effectively used to understand and/or design novel energy materials.

## Theme1:

Battery Materials

### Project#1: Anti-Perovskite Solid-State Electrolytes

A first step towards having a practical solid-state electrolyte is to be synthesizable and thermodynamically stable against decomposition. With the help of DFT methods and the robust, open-source Python library for materials analysis (pymatgen), one can generate the phase diagrams of the material of interest, which allows him to get a comprehensive understanding about the stability of the material.

Pymatgen also allows us to study the electrochemical stability of the electrolyte. Together with a look onto the density of state of the electrolyte, one can get an overall picture about the electrochemical stability of the electrolyte.

Not only this, but also DFT methods allow us to estimate key mechanical properties of the electrolytes including its bulk, shear, and Youngs modulus. This is particularly useful when the experimental characterization of the material is challenging (e.g., in case of hygroscopic electrolytes).

Another remarkable advantage of atomistic simulation is its ability to give us detailed understanding about the diffusion process of the mobile ions in the electrolyte and the structure-conductivity relationship. By running ab-initio molecular dynamics simulations, one can generate the trajectories of all specie in the material under study. By performing the appropriate theoretical and statistical analysis on the trajectories, one can get several valuable information including: diffusion coefficients, activation energies, hopping mechanism (e.g., concerted or individual), etc.

The capabilities that atomistic simulations can give us to understand the properties and behavior of materials are numerous.

The above is just a sample!

Selected Publications:

[1] Effat, M.B., Liu, J., Lu, Z., Wan, T.H., Curcio, A. and Ciucci, F., 2020. Stability, Elastic Properties, and the Li Transport Mechanism of the Protonated and Fluorinated Antiperovskite Lithium Conductors. ACS Applied Materials & Interfaces. [link]

[2]Liu, J., Lu, Z., Effat, M.B. and Ciucci, F., 2019. A theoretical study on the stability and ionic conductivity of the Na11M2PS12 (M= Sn, Ge) superionic conductors. Journal of Power Sources, 409, pp.94-101. [link]