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CH413 - Advanced Computational Chemistry

  • Module code: CH413
  • Module name: Advanced Computational Chemistry
  • Department: Chemistry
  • Credit: 15

Content and teaching | Assessment | Availability

Module content and teaching

Principal aims

This module is designed to develop student knowledge of, and competence in practicing, state-of-the-art methods in computational chemistry. The module will be equally split between three members of staff in the computational chemistry section (Habershon, Sosso and Maurer) in the Department of Chemistry. In each section of the module, students will be introduced to contemporary research challenges in three distinct areas of contemporary computational chemistry: (i) Enhanced Sampling and Machine Learning methods. (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 (ii) Density functional theory and materials modelling. (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. (iii) Time-dependent quantum dynamics. (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. In each section, learning will be supported by 4 “methodology” lectures (4 x 1hr), 1 “applications” lecture (1hr) illustrating how the three computational topics above can be used to address contemporary chemical challenges, and a computational workshop (2hrs per section) giving students a chance to see how computational chemistry methods are implemented in code examples.

Principal learning outcomes

By the end of the module the student should be able to. Evaluate the applicability of different computational chemistry methods in different chemical problems. • Describe the basics of the software implementation of different computational chemistry methods. • Discuss the contemporary challenge areas in the field of computational chemistry. • Work with simple Python scripts of use for scientific computing • Apply the discussed computational chemistry methods in the context of the covered examples and workshop problems

Timetabled teaching activities

15 x 1 hr lectures 3 x 2 hrs computational workshops

Departmental link

go.warwick.ac.uk/CH413

Module assessment

Assessment group Assessment name Percentage
15 CATS (Module code: CH413-15)
D (Assessed/examined work) 1.5 hour examination (April) 100%

Module availability

This module is available on the following courses:

Core

N/A

Optional Core

N/A

Optional
  • Undergraduate Master of Chemistry Variants (F105) - Year 4
  • Undergraduate Master of Chemistry (with Intercalated Year) (F107) - Year 5
  • Undergraduate Master of Chemistry (with International Placement) (F109) - Year 4
  • Undergraduate Master of Chemistry Variants (F109) - Year 4
  • Undergraduate Master of Chemistry (with Industrial Placement) (F110) - Year 4
  • Undergraduate Master of Chemistry Variants (F110) - Year 4
  • Undergraduate Master of Chemistry (with International Placement) (F111) - Year 4
  • Undergraduate Master of Chemistry (with Industrial Placement) (F112) - Year 4