Artificial Super Intelligence

Advanced simulation for organic photo-voltaic (OPV) In order to predict the quantum efficiency of a organic photo-voltaic (OPV) devices, molecular-level modelling and simulation approach can be used.  The three computation parts are calculated to predict the quantum efficiency of organic photo-voltaic (OPV) devices. The morphology modelling part, exciton dissociation part and charge transport part are the three important parts to be modelled for performance evaluation.  We focus on a bulk hetero-junction (BHJ) OPV devices since it is known to achieve high quantum efficiency because of accelerating exciton dissociation efficiency.  Morphology modeling It has been known that the performance of organic material device is highly relied on morphology of organic material layers. In particular, the domain size and the percolation paths in morphology of active layers are critical factors to determine the performance of the organic photovoltaic device. Exciton dissociati...

I am writing a Python codes for developing MIMO classes. Up to now, I have written my simulation codes for MIMO in Matlab. Recently, I realize that there is limitation to apply object oriented concept in Matlab programming compared to other OOP oriented languages such as Python. I now made my mind to write python codes for communication system simulations including MIMO algorithms. MIMO algorithms can be implemented by a Python code based on numpy, scipy as well as matplotlib. I found that now matplotlib offers various plotting tools, such as 2D plots, 3D plots, animation and even, saving animation pictures as mpeg files. [Parallel Processing] I found that Python also supports parallel computing for SMP, MPI and GPU systems. The related toolbox is PP. I guess that there is a computation engine supporting parallel processing.

The kinetic Monte Carlo (kMC) algorithm with first reaction method (FRM) can be appiled for the charge transport simulation of the organic photovoltaic cells. kMC can be used for all three parts which are modelings for morphology generation, exciton dissociation and charge injection/extraction. 1. Morpology Modeling  For morphology modeling, the Ising Hamiltonian method can be used. First, random morphology is generated by applying random position p and n materials. If the size of sites is L * L * L, around halft sites ~ L * L * L /2 are set to p materials such as +1 for each element of the L * L * L morphology matrix while the other half sites are set to n materials such as -1, Note that the total number of p-material and n-materials sites must be equal to the total number of sites, which is L * L * L. That is, any site is belonging to either p or n-material. 2. Exciton Dissociation Modeling Exciton dissociation can also be modeled by kMC algorithm with the first reaction...

The kinetic Monte Carlo (kMC) is a simulation method for Physics and Chemistry. Monte Calo (MC) is a widely using mathematical method for simulation, while KMC is especially useful for dynamic system simulations. It includes a timing factor