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Neural Network as a Cost Function for EPSO Algorithm in Perovskite Solar Cell Simulation
  • Daniyal Khosh Maram,
  • Hamed Abnavi
Daniyal Khosh Maram
Hamed Abnavi


Time-consuming is one of the main bottlenecks in the SCAPS-1D simulations in the case of more computational data set. In this regard, we are achieving outputs data in SCAPS-1D and repeat this simulation by employing a neural network as a cost or target function for the evolutionary particle swarm optimization (EPSO) algorithm to decrease the computational expensiveness of SCAPS-1D simulation. Optimization and numerical simulation tools pave the way for having a better insight into the designing of perovskite solar cells. Also, it allows finding a relation between artificial intelligence and device physics.