Application of Neural Networks: A Molecular Geometry Optimization Study
نویسندگان
چکیده
Optimization algorithms are iterative procedures that evolve from guessed starting points (SP) to the desired global minimum. Their performance can be greatly improved [1], if a neural network (NN) is created to select suitable SP. Here, we have trained NN’s to select possible groundstate geometries for silicon clusters. A genetic algorithm uses these candidates as initial population and performs the final energy optimization. For convenience, cluster’s geometry is described as a piling up of plane layers of atoms.
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