The scientific field addressed by this postdoctoral project lies at the intersection of ab initio molecular dynamics, machine learning, and the thermodynamic characterization of materials under extreme conditions. Traditional AIMD simulations are a powerful tool to study temperature- and pressure-dependent properties from first principles, but their high computational cost limits their widespread use. By developing and applying machine learning-assisted sampling techniques like MLACS, this postdoc aims to drastically reduce the computational burden while retaining ab initio accuracy. This enables the efficient exploration of phase diagrams in high-pressure and high-temperature conditions. This research contributes to both fundamental understanding and practical modeling of materials, offering high-impact tools for the scientific community.