Predicting the Fracture Propensity of Amorphous Silica Using Molecular Dynamics Simulations and Machine Learning
This study used molecular dynamics and machine learning to investigate how atomic structure affects amorphous silica's fracture. They found that decreasing coordination number (DCN) is key to fracture propensity, which can be predicted from the initial atomic configuration. The AI system accurately distinguishes DCN states and links them to crack locations and macroscopic mechanical properties based on quenching pressure.