(7cl) Design of Advance Materials by Using ab initio Structural Search

Research Interests: Crystal structure prediction, X-ion batteries (X=Li, Na, K, Mg,), Lightweight materials, super hard materials, molecular dynamics, transport properties

Teaching Interests: Inorganic chemistry, Physics, Thermodynamics, Computational physics

In solid-state physics, it is well known that the arrangement of atoms in a crystal structure determines almost all physical and chemical properties of a given material. In recent years, the discovery and computational design of novel materials has been performed by exploring similar compounds and structures to the one of interest, experimentally as well as theoretically. However, an open question remains as how we can proceed if we do not know both the structure and composition? Can we predict stable structures based on the knowledge of the chemical composition alone? In recent years, progress in computational materials science has helped the development of structural search algorithms that have tried to solve these questions.

Currently, crystal structure prediction is one of the most important fields of study and an open challenge. This is because the only input needed is the chemical composition and external conditions. Furthermore, novel materials with specific chemical and/or physical properties can be predicted (i. e. hardness, band gap, density, dielectric constant, etc.). Also, we can study systems under extreme conditions such as high pressure and high temperature. With the help of these methodologies the cost of production and design of new materials can be reduced, as well as the number of potential experiments. Another advantage of crystal structure prediction is that multiple compositions and conditions can be theoretically studied. Additionally, their properties can be studied before they are synthesized. Very recently, novel methodologies to predict new crystal structures have been developed. In some of these methodologies, the search is based on experimental experience where the information is stored in databases. Then, novel materials can be predicted by using this information along with the help of methods developed in data mining. Other methods are based on exhaustive searches of stable structures over the potential energy surface. Here, the main goal is to find the global minimum, which corresponds to the most stable structure (ground state). Some of these methods are random search, particle swarm, evolutionary algorithms, basin hopping, and minima hoping.

In my future work as faculty, I would like to continue working in the crystal structure prediction field. My research will be focus in tree different topics: 1) lightweight materials, 2) superhard materials, and 3) X-ion batteries (X=Li, Na, K, and Mg).