معرفی
Carl Hemprich is a doctoral researcher at the Department of Mechanical and Process Engineering (D-MAVT), affiliated with ETH Zürich. His work bridges thermodynamics and machine learning for property prediction and molecular design in energy systems.
- PhD candidate in Energy and Process Systems Engineering
- Active in computational methods for chemical engineering
Research Focus: Digital Chemistry, molecular property prediction, and sustainable refrigerant development for high-temperature heat pumps. His group-contribution methods for PCP-SAFT modeling have advanced dipolar molecule analysis and cis-trans isomer differentiation. Publications span ACS Omega, International Journal of Refrigeration, and conference proceedings.
Notable Trends: Integration of machine learning with thermodynamic modeling (e.g., PCP-SAFT), optimization of high-glide refrigerant blends, and in silico catalyst design. His work supports sustainable energy technologies and low-GWP refrigerants.
- Open-source Python package for vector-based GC methods
- SNF grant 203645 for high-temperature heat pump research
- Collaborative projects with André Bardow, Kai Leonhard, and interdisciplinary teams
