Studying the mechanics of TiB via molecular dynamics and machine learning

This thesis investigates the mechanical properties of titanium boride (TiB) using a combined approach of molecular dynamics (MD) simulations and machine learning (ML) to address the growing demand for high-performance materials in industries like aerospace, automotive, and biomedical engineering. TiB, known for its exceptional hardness, stiffness, and thermal stability, is analyzed in various simulated conditions to bridge existing knowledge gaps regarding its mechanical response under different environmental and structural stressors.Molecular dynamics simulations are conducted to understand TiB’s behavior at an atomic level, focusing on the effects of temperature and defect variability on its mechanical properties, including Young’s modulus, tensile strength, and Poisson’s ratio. This study offers insights into TiB's elasticity and resilience under real-world conditions by creating simulations across a temperature range from 100 K to 2500 K and defect concentrations of 2% and 5%. The results demonstrate that while TiB exhibits high stiffness and strength, its performance is sensitive to thermal and structural changes, underscoring the importance of defect management and thermal considerations in practical applications.