Computational Materials Science: A Powerful and Predictive Tool

Materials science is an interdisciplinary area with a strong link to the fundamental sciences, chemistry, and physics. While chemistry is needed to understand the composition of natural materials and to create new ones, in physics we obtain the understanding of the materials-property relation, the precondition to suggest new applications, and to provide the basis for developing better-performing materials. Our standard of living today has been largely determined by past discoveries of “new” materials, and our future prosperity will depend to a large extent on the fruits of contemporary research into even newer materials and innovative processing routes.

Advanced materials with improved functionality however, share a common characteristic: They are complex. Achieving the required performance gains depends on exploiting the many degrees of freedom of materials development including multiple chemical components, nanoscale architectures, and tailored electronic structures. This introduces enormous complexity in the discovery process, complexity that must be understood and managed. We do not have the time or resources to explore all the options experimentally or by trial and error. The only solution is by design, using new synthesis and characterization tools, theory, and simulation and modeling to understand complex materials and chemical systems and predict the most promising research directions. Sifting through the options using predictive modeling is the only intelligent and efficient path forward. Predictive capability is also driving the transformation of technological innovation. Integrated computational materials engineering has been shown to accelerate the introduction of new materials and processes into the product development cycle by minimizing testing requirements, reducing failures, and increasing quality.

Modern computational materials science is fueled by theoretical solid-state physics, allowing the atomistic and quantum-theoretical description of solids. With electronic band structure calculations the fundamental link between structure and electronic structure of materials was elucidated, and the prediction of materials with interesting electronic, magnetic, or optoelectronic properties became possible (Martin, 2008). Theoretical calculations have also become powerful tools to investigate and describe interface phenomena including adsorption, surface chemical reactions, and heterogeneous catalysis.

Among the theoretical methods, density functional theory (DFT), which computes ground-state energy and its derived properties using electron density instead of wave functions, has substantially reduced computational cost and makes possible calculation of relatively larger systems such as nanoparticles and periodic surfaces. In particular, the first-principles derived energetics, atomic configurations, transition states, energy barriers, and reaction channels can be used to predict the catalytic activities at an atomic level. Furthermore, DFT calculations can also be used to validate the experimental observations or to give explanations in depth.