García Gallardo, Kevin Amin

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    Estudio de las transiciones de fase por medio de "machine learning"
    (2021-12-09) García Gallardo, Kevin Amin; Ramos, Rafael A.; College of Arts and Sciences - Sciences; Santana Colón, Samuel A.; Mendoza Centeno, Frank W.; Department of Physics; Zapata Medina, Rocio
    Machine Learning (ML), a subfield of Artificial Intelligence (AI), has become a robust area widely studied in pattern recognition, in which large bases of data can be processed, guaranteeing the reduction of the dimensionality with minimal information loss. With ML, certain physical quantities can be determined without the need to have prior knowledge of the problem or to use complex physical systems. In this work, we used Principal Component Analysis (PCA), an unsupervised learning technique, and Artificial Neural Networks (ANN), a supervised learning technique, to determine the transition coverage and identify the possible phase transitions that are present during the adsorption of adsorbates on a metallic surface. To simulate the phenomena of repulsion of the adsorbates with the closest neighbors and the attraction with the next closest neighbors, a Lattice- Gas Model (LG) was implemented using the grand canonical collective for different square networks, Due to the stochastic nature of the problem, the Metropolis algorithm was applied with Monte Carlo simulations in equilibrium. From the results obtained in this research, it was shown that PCA and ANN techniques can identify phase transitions, in the same way, it was possible to obtain the critical potential close to the real thermodynamic value in which the phase transition occurs. Based on these results, it can be shown how long-range interactions influence the properties of these phase transitions. All these results confirm that ML is satisfactory when studying complex systems. Both the PCA and the ANN can be adapted and learned from previous interactions using complex data, without the need of explicit human intervention.