Abstract
Theoretical determination of the ground-state geometry of Si clusters is a difficult task. As the number of local minima grows exponentially with the number of atoms, to find the global minimum is a real challenge. One may start the search procedure from a random distribution of atoms but it is probably wiser to make use of any available information to restrict the search space. Here, we introduce a new approach, the Assisted Genetic Optimization (AGO) that couples an Artificial Neural Network (ANN) to a Genetic Algorithm (GA). Using available information on small Silicon clusters, we trained an ANN to predict good starting points (initial population) for the GA. AGO is applied to Si10 and Si20 and compared to pure GA. Our results indicate: i) AGO is, at least, 5 times faster than pure GA in our test case; ii) ANN training can be made very fast and successfully plays the role of an experienced investigator; iii) AGO can easily be adapted to other optimization problems.
Highlights
Artificial Neural Networks (ANN) and other artificial intelligence algorithms have proved to be very useful tools in theoretical and experimental Chemistry
Other important applications are: i) comparison of ANN with quantum mechanical techniques for the prediction of molecular properties for inorganic systems2. ii) predictions, made by Sigman and Rives[3], of atomic ionization potentials using shell model parameters as input data for the ANN. These applications encourage us to explore the potential of ANN in yet another field: the prediction of the ground-state geometry of clusters
We want to associate ANN to a quantum chemistry method to search for the geometry of the ground-state of silicon clusters
Summary
Artificial Neural Networks (ANN) and other artificial intelligence algorithms have proved to be very useful tools in theoretical and experimental Chemistry. Genetic algorithms[14,15,16] and simulated annealing[13,17] are optimization methods that do not depend on the calculation of gradients. They imitate natural processes and they are able to overcome barriers to avoid local minima. We want to associate ANN to a quantum chemistry method to search for the geometry of the ground-state of silicon clusters. Avoiding sequences of layers that ANN predicts as unfavorable we can keep the search algorithm from wasting valuable time In this case, ANN learning power plays the role of an experienced investigator. We discuss, the architecture of the ANN and the results obtained by the combination of the classifier–ANN with the genetic algorithm
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