Materials used in energy applications, often undergo structural transformation that are crucial to the operation of the device. We have looked at three different materials to understand various aspects of their structural evolution at long time scale. These materials include i) Si used as an anode in lithium ion battery, ii) nanoporous metal alloys used as catalysts and iii) metal nanoparticles which is again used in catalysis. During lithiation/delithiation process, an amorphous lithiated phase is formed which is separated from the pure Si phase with a sharp phase boundary. The entire lithiation/delithiation process majorly depends on the migration of this phase boundary. When any lithiation occurs, due to presence of Li atoms in the system, we observe certain amount strain evolved. We have studied the effect of strain on the lithium diffusion barrier in bulk Si using nudged-elastic band (NEB) method applying modified embedded atom method (MEAM) as interatomic potential. Nanoporous material is usually obtained by dealloying method where electrochemically active species undergoes dissolution process whereas electrochemically noble material agglomerates and forms new cluster of islands resulting in formation of several ligaments and nodes. To understand the structural morphology of these nanoporous materials, we need a characterization tool which can quantify them correctly. So we have developed a characterization technique to calculate the size (length/diameter/area) distribution of nanoporous ligaments and facets using of connectivity lists for sites. Understanding the behavior of different response surface like extent of dissolution, surface area of various facet planes with perturbation of different operating conditions like temperature, binding energy, composition or dissolution pre-factor is also important because this will help to provide an insight about the morphological evolution of different nanoporous structure and importance of parameters in dealloying process. A response surface model analysis for different dealloying times varying with different important controllable parameters is performed to understand the insight how different responses change with a small perturbation in any of the controllable parameters. Both of these systems are very complicated. Understanding the detailed kinetics of these systems is not straight forward. Novel simulation techniques are required which can systematically determine the relevant pathways required to understand the kinetic mechanism of the complete system. We have developed a MD based KMC simulation technique to determine the relevant pathways systematically. We have applied our framework on an Ag cluster system to understand the detail mechanism of relevant pathways.
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