Abstract
Quantum mechanical ab initio calculation constitutes the biggest portion of the computer time in material science and chemical science simulations. As a computer center like NERSC, to better serve these communities, it will be very useful to have a prediction for the future trends of ab initio calculations in these areas. Such prediction can help us to decide what future computer architecture can be most useful for these communities, and what should be emphasized on in future supercomputer procurement. As the size of the computer and the size of the simulated physical systems increase, there is a renewed interest in using the real space grid method in electronic structure calculations. This is fueled by two factors. First, it is generally assumed that the real space grid method is more suitable for parallel computation for its limited communication requirement, compared with spectrum method where a global FFT is required. Second, as the size N of the calculated system increases together with the computer power, O(N) scaling approaches become more favorable than the traditional direct O(N{sup 3}) scaling methods. These O(N) methods are usually based on localized orbital in real space, which can be described more naturally by the real space basis. In this report, the author compares the real space methods versus the traditional plane wave (PW) spectrum methods, for their technical pros and cons, and the possible of future trends. For the real space method, the author focuses on the regular grid finite different (FD) method and the finite element (FE) method. These are the methods used mostly in material science simulation. As for chemical science, the predominant methods are still Gaussian basis method, and sometime the atomic orbital basis method. These two basis sets are localized in real space, and there is no indication that their roles in quantum chemical simulation will change anytime soon. The author focuses on the density functional theory (DFT), which is the most used method for quantum mechanical material science simulation.
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