A synopsis of the curriculum
This module will introduce the student to the growing field of computational chemistry and its viability as a cost-effective alternative to experiment that provides unique insight. It is important that a chemical sciences graduate is trained in this area because many peer reviewer publications in physical, inorganic and organic chemistry include a computational component. The module will run primarily as a set of computational labs with written materials and recorded lectures provided online for delivery of the understanding, background and application of the methods used in the laboratory sessions, which will include:
Classical Mechanics Atomistic Simulation, Force-fields, Energy Minimisation,
Molecular Dynamics, Monte Carlo
Quantum Mechanics Density Functional Theory, Hartree-Fock theory,
Wave-Function mechanics
Simulation Codes Examples may include for example: DL_POLY, GULP (classical mechanics), Gaussian, Castep, Dmol (quantum mechanics)
The experiments will cover the use of computer modelling to explore the structure, properties, processes and applications of organic and inorganic materials. Typically, they might comprise:
• Simulating the adsorption of molecules on surfaces (catalysis)
• Calculating the density of states and phonon modes of materials (band gap)
• Calculating activation energy barriers of a chemical reaction (organic chemistry)
• Simulating diffusion processes (fuel cells, battery materials)
• Simulating (hard, soft) systems at the mesoscale, such as surfactant-polymer interactions and architectures
• Quantitative Structure–Activity Relationship (QSAR) models; the application of descriptor calculations and statistical modelling to design new molecules.
• Machine Learning –intelligent computer-aided design of new materials
The final experiment (mini project) will be one of the students own choosing where they will plan, design and formulate a computational experiment using any computational method available and then appraise the reliability and intellectual or commercial value of the experiment.
Blended Distance learning:
Contact Hours: 96
Private Study Hours: 54
Total Study Hours: 150
Laboratory Report – weighted 25%
Poster – weighted 25%
2 hour examination – weighted 50%
The pass mark for each individual assessment is 40%. All assessments must be passed in order to pass the module
P.W Atkins, (1998) Physical Chemistry, Oxford University Press.
R. Chang, (2000) Physical Chemistry for the Chemical and Biological Sciences, Sausalito, California: University Science Books.
Handbook of Computational Chemistry Springer eBooks, Heidelberg, Germany: Springer-Verlag Berlin Heidelberg, 2012.
Relevant reviewed scientific journals.
See the library reading list for this module (Medway)
The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
Provide a critical understanding of the field of computational chemistry.
Show how computational chemistry can provide unique insight to complement experimental chemistry.
Show how computational chemistry can deliver understanding in areas that are not, thus far, accessible to experiment.
Understand methods of computational chemistry in depth, spanning hierarchical length and time scales including: quantum mechanical, molecular dynamics (atomistic), mesoscale modelling and molecular graphics.
Use computational methods to calculate the structure, properties and processes of materials.
Evaluate computational chemistry critically with regards to scope and limitations.
Plan, design and formulate a simulation (or set of simulations) that realise a truly predictive capability.
The intended generic learning outcomes.
On successfully completing the module students will be able to:
Perform effective research costing and planning (health and safety, ethics); 'simulation vs experiment'.
Apply problem-solving skills, relating to qualitative and quantitative information, extending to situations where evaluations have to be made on the basis of limited information.
Optimise time-management and organisational skills, as evidenced by the ability to plan and implement efficient and effective modes of working.
Enhance interpersonal skills, relating to the ability to engage with others and to engage in team working within a professional environment.
Demonstrate the ability to exercise initiative and personal responsibility; the ability to make decisions in 'unchartered', complex and unpredictable situations; the independent learning ability required for continuing professional development.
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