Development of Mg/Al/Si/O ReaxFF Parameters for Magnesium Aluminosilicate Glass Using an Artificial Neural Network-Assisted Genetic Algorithm

J Yeon and SC Chowdhury and CM Daksha and JW Gillespie, JOURNAL OF PHYSICAL CHEMISTRY C, 125, 18380-18394 (2021).

DOI: 10.1021/acs.jpcc.1c01190

Commercial high-strength S-glass fiber used in structural composites mainly consists of SiO2, Al2O3, and MgO. There is no established reactive force field to characterize S-glass fiber. In this study, a newly developed artificial neural network (ANN)-assisted genetic algorithm (GA) is applied to optimize a new ReaxFF parameter set to describe Mg/Al/Si/O interactions in S-glass and other magnesium aluminosilicate (MAS) glass compositions. The training set includes the density functional theory data of the energy response of various Mg/Al/Si/O crystals during volumetric expansion and compression and Mg migration inside Mg/Al/Si/O crystals. Test molecular dynamics simulations showed the characteristics of tectosilicate MAS glasses. Different structural properties, including oxide coordination, density, structural factors, and mechanical properties, showed fair agreement with references from experiments and other simulations. A newly developed GA-ANN parametrization algorithm assisted the training process. This force field can be used for virtual composition mapping to develop new glass fiber materials. We also believe our force field would support computational studies of mechanical properties of amorphous materials used in geochemistry, construction, and protective material applications.

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