Computational Materials Science tools, based in chemistry and physics, give us: (i) Qualitative frameworks for thinking about atomistic processes and mechanisms, (ii) Quantitative understanding of thermodynamic driving forces, and (iii) Prediction of properties or molecular architectures for engineering design. Often, we will want to know the structure of a few atoms in a material (e.g., defect or reactive sites), and quantum mechanics allows us to calculate these structures and associated electronic energies to high accuracy. However, we ultimately need to predict multi-scale properties that can be compared with experimental data, so we use statistical mechanics to perform temporal or spatial averages over a large number of simulations to obtain these macroscopic observables. We thus develop predictive insight that may be used to guide experimental design of new materials.
This is particularly applicable to the design of Materials for Energy Harvesting & Storage, where we are particularly interested in controlling the electronic and optical properties of these materials. From the electronic standpoint, we are interested in determining which structural and compositional changes may enhance the absorption of photons and excitation of electrons to higher energy states, for applications ranging from conducting electrodes in thin film solar cells to thermoelectric energy conversion. From the optical standpoint, we are interested in determining how the size of nanostructures modulates optical activities, such as colors and polarizations, and guiding experimental fabrication efforts on related materials.