My research focuses on the development and application of computational methods to discover and design novel energy materials, and study their properties, in a multidisciplinary field where physics, chemistry, materials science and computer science intersect.
I have developed several methods for atomistic simulations, mainly on structure prediction techniques based on the minima hopping method as implemented in the Minhocao package. Furthermore, I have been involved in the development of structural fingerprints and transition state search methods to explore the energy landscape of chemical systems. I have also contributed to the BigDFT density functional theory package and the Alborz atomistic simulation toolkit for neural network potentials as a developer. Currently, I am working on developing a fully integrated library for atomistic simulation environments (FLAME), which will be released as an open source package for public use.
I have been applying my own methods together with state-of-the art algorithms to study and understand condensed matter at various conditions. The tools that I use include, but are not limited to, structural search, data mining, high-throughput, lattice dynamics calculations, and machine learning, mostly in conjunction with density functional theory or other methods for atomistic modeling. The focus of my research lies in studying low-dimensional systems such as nano-particles, surface adsorption mechanisms, surface reconstructions, and 2-dimensional materials, but also in investigating properties of bulk crystalline materials including superconductivity, hydrogen storage materials, materials at high pressures, thermoelectric materials, materials for photovoltaic applications and transparent conducting oxides.