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We live in a mathematically consistent physical universe. For centuries we improved our understanding of the universe by developing mathematical models (theories) or physical probing devices like telescopes or microscopes. In the last few decades, with increasing computational power, we started to think about everything in terms of information. To his end, we now consider the possibility of our universe being just one of the infinitely many universes woven with mathematical consistency out of information fabric. Nowadays, we use computers to probe the universe at all scales, ranging from atoms to galaxies. Computation become a crucial tool bridging the gap between theoretical and experimental studies.

A century after quantum mechanics was conceived, we are now able to predict the stability and physical properties of materials that don’t even exist in Nature. Our predictions have enough precision to guide or understand experimental studies. This precision is reached via implementation of elegant theories, like the Density Functional Theory (DFT), in efficient algorithms that run in powerful supercomputers.

Employing the state-of-the-art tools developed for computational condensed matter physics we investigate atomic, electronic, mechanical, thermal and other properties of novel low-dimensional materials. These include two-dimensional (2D) materials, like graphene, silicene, germanene, metal dichalcogenides, and one-dimensional (1D) materials, like transition metal atomic chains, carbon atomic chains and nanotubes, silicene nanoribbons, Ga/As nanowires and etc. In this respect, we use our tools to explore the portion of the computational universe that corresponds to our physical reality. Hence, the novel materials and properties that we discover there can be realized here with experiments.

Our physical universe is painstakingly explored by ever stronger telescopes in search of places that can inhabit life. This ambitious search is powered by pure curiosity of humankind and belief in making plans that exceed our current lifetimes by orders of magnitudes. We would like to join this search by exploring the computational universe in search of intelligent life. In this respect, we started developing computational models that mimic the most primitive neural circuitries encountered in biological organisms. We collaborate with experts from various disciplines and our models use concepts borrowed from disciplines ranging from statistical physics to machine learning.