THESIS
2020
x, 63 pages : illustrations (some color) ; 30 cm
Abstract
Water is an important substance on Earth. The dielectric constant of water is a crucial
quantity affecting solubility of salts in water. However, it is experimentally challenging to
study water under high pressure and high temperature. A typical method to study the dielectric
constant of water under extreme conditions is molecular dynamics. Here, I applied
the machine learning method combined with molecular dynamics to compute the dielectric
properties of water. Our neural network model achieves both high accuracy and high computational
efficiency. In addition, I performed first-principles calculations to study the elastic
properties of ice VIII and ice X with different exchange-correlation functionals. The elastic
constant could be an indicator for the phase transition between...[
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Water is an important substance on Earth. The dielectric constant of water is a crucial
quantity affecting solubility of salts in water. However, it is experimentally challenging to
study water under high pressure and high temperature. A typical method to study the dielectric
constant of water under extreme conditions is molecular dynamics. Here, I applied
the machine learning method combined with molecular dynamics to compute the dielectric
properties of water. Our neural network model achieves both high accuracy and high computational
efficiency. In addition, I performed first-principles calculations to study the elastic
properties of ice VIII and ice X with different exchange-correlation functionals. The elastic
constant could be an indicator for the phase transition between ice VIII and ice X.
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