Amorphous materials modeling and classification for low mechanical loss mirror coatings using machine learning methods*

Instead of the well-defined atomic structures of crystals, amorphous materials are more complicated due to intrinsic randomness. Modeling and predicting the properties of amorphous materials (amorphous Ta₂O₅, doped Ta₂O₅) are important to understand experimental results and to find lower mechanical loss mirror coatings to reduce thermal noise in the next generation of LIGO laser interferometer gravitational wave detectors. In our work, thousands of atomic models of amorphous materials are generated using reverse Monte Carlo (RMC) and molecular dynamics (MD) simulations based on experimental data. Classifying them into different groups according to their properties and features with the help of machine learning, enables us to understand the differences between these models and use the information from these structures to find the best materials for low mechanical loss mirror coating.