Warwick Advanced Computational Chemistry material
This module has three parts. The lecture material for each part are contained in respective folders
Enhanced Sampling and Machine Learning methods (G. Sosso)
Methodological challenges: a) Enabling molecular simulations of “rare events” such as chemical reactions and phase transitions; b) Taking advantage of molecular datasets to predict the functional properties of new chemical species.
Application domain: a) Crystal nucleation and growth; b) Drug discovery
Density functional theory and materials modelling (R. J. Maurer)
Methodological challenges: Achieving chemical accuracy for interactions between molecules, and between molecules and surfaces; enabling computationally efficient evaluation of structural, thermodynamic, and spectroscopic materials properties in the mesoscopic regime
Application domain: (a) Hybrid and composite materials prediction, (b) Heterogeneous photo- and electrocatalysis.
Time-dependent quantum dynamics (S. Habershon)
Methodological challenges: Efficient propagation of time-dependent wavefunctions and density matrices, determination of accurate potential energy surfaces for quantum dynamics, and accounting for non-adiabatic effects.
Application domain: Photochemistry of organic and biological molecules, light-harvesting for energy applications.