Quantum Chemistry Workflow at Scale on HPC Systems

Quantum chemistry can predict molecular structure and properties from first principles — but accuracy comes at a computational cost. This course focuses on building efficient, scalable quantum chemistry workflows for multi-node CPU clusters.

Participants will perform geometry optimizations with DFT, validate structures through vibrational analysis, and explore higher-accuracy methods such as MP2 for energy refinement. The course highlights the trade-offs between accuracy, runtime, and scaling across different electronic structure methods.

HPC is integrated throughout, covering parallel calculations, resource allocation, performance monitoring, and scaling behavior on distributed systems. Practical exercises demonstrate how method choice and system size affect CPU, memory, and inter-node communication demands.

By the end of the course, participants will be able to design and execute complete, reproducible quantum chemistry workflows – from structure preparation to energy refinement – and efficiently run them on modern HPC systems for applications in bio and materials chemistry.