Erik Laurini
(University of Trieste)

Advancements in molecular simulation algorithms and computational power have significantly bridged the gap between experimental structural data and molecular dynamics, enabling a deeper understanding of structure-function relationships. Molecular modeling plays a crucial role in biological and industrial applications by describing molecular interactions at the atomistic level and quantifying their thermodynamics and kinetics.
Free energy estimation, essential for characterizing molecular interactions, can be achieved through various computational approaches. Highly accurate methods like Free Energy Perturbation (FEP) and Thermodynamic Integration (TI) are powerful but computationally expensive. High-Performance Computing (HPC) can help overcome these challenges, improving efficiency and convergence.
Beyond thermodynamics, kinetics is critical yet computationally demanding. Infrequent metadynamics has proven effective in capturing kinetic information and benefits greatly from HPC resources. This seminar will explore how HPC-based techniques enhance the predictive capabilities of molecular simulations for diverse applications.