(5s) Multiscale Modeling for Energy Applications

The goal of our research is to understand thermodynamics, mechanics and transport in materials for energy applications, i.e., energy generation (fuel cells) and energy storage (batteries and capacitors) devices, using multiscale modeling. This understanding can be employed for a) improving efficiency and performance, and reducing cost of current technologies and b) discovering new materials to counter challenges faced by existing technologies.

Materials used in energy generation/storage devices typically entail phenomena involving several atomic/molecular species and thermodynamic phases and span multiple length and time scales. The device material structure-property relationships are generated using reliable multiscale models that we have developed in our group, and systems analysis methods. Four different multiscale models are employed. The temperature accelerated dynamics (TAD)-based kinetic Monte Carlo (KMC) method is used to efficiently generate an on-the-fly KMC process catalog with a given confidence. Adaptive coarse-grained Monte Carlo (CGMC) method is derived from a KMC model for performing accelerated KMC simulations. Information theory based a posteriori error estimates are derived for error control in the CGMC method. The tau-leap method is developed for accelerated KMC simulations in time. The continuum mesoscopic equations are derived from KMC models in the limit of infinite interaction lengths. The accuracy and computational requirements of these multiscale models decreases in the order mentioned.

The strengths of our multiscale framework enable predictive materials modeling for energy application. The correct atomistic physics, such as interactions, mechanisms, thermal fluctuations and correlations are incorporated. This is essential for accurately predicting material properties, such as phase behavior and dynamics. A systematic derivation of each of the multiscale models directly from an atomistic model ensures seamless exchange of information between different multiscale models. Thus, depending on the accuracy requirements different model descriptions can be employed in different regions of the material. The aforementioned multiscale models overcome the inherent material length and time scale separation. Studies on kinetics and thermodynamics in multicomponent materials at device length and operation time scales are therefore possible.

Application of the multiscale models to three broad problem areas in rational design of materials for energy generation and storage are illustrated next.

Role of material structure in ionic mobilities

An understanding of ionic transport is essential for designing better materials for energy storage applications. However, the atomic mechanisms prevalent at time scales of interest are typically not accessible to molecular dynamics, and are therefore not well understood. Diffusion and ionic conduction in oxide materials is studied using the TAD-based KMC method. The role of nanostructure up to microstructure, e.g., the grain size and interfaces, in determining ionic mobilities is elucidated.

Role of defects interactions in materials structure, phase diagram and stability

Defect formation and propagation in materials is a complex phenomenon that spans atomic length and time scales (nm and ps, respectively) to macroscopic scales at the microstructure level (mm and min, respectively). Large concentration of defects in a material crystalline structure can be created by intentional addition of substitutional/dopant atoms. Depending on the defect source and the material, different defect shape and size distributions are observed, which ultimately determines the physico-chemical, mechanical and electrical properties of the material. We present fundamental insights obtained from the multiscale framework regarding material structure, phase diagram, segregation and stability.

Control of material structure for tuning material properties

Material properties, such as ionic transport, can be controlled indirectly by tuning nanostructure features. Bottom-up fabrication strategies based on self-assembly offer exciting possibilities for inexpensively manufacturing nanostructures. However, control of self-assembly to obtain the desired nanoparticle size, shape, density, and lateral arrangement is still a major challenge. We present a multiscale modeling based optimal control strategy that we developed recently, for controlling self-assembled structures.