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Module

CHY2610 : Scientific Computing for Chemists (Inactive)

  • Inactive for Year: 2020/21
  • Module Leader(s): Dr Daniel Cole
  • Owning School: Natural and Environmental Sciences
  • Teaching Location: Newcastle City Campus
Semesters
Semester 2 Credit Value: 10
ECTS Credits: 5.0

Aims

To describe the role of scientific computing in chemistry; to consolidate the use of python as an example of a programming language; to build and employ computational models and simulations in chemistry research

Outline Of Syllabus

The role of scientific computing in chemistry
• case studies, including molecular modelling and artificial intelligence
• computer hardware and software

The python coding language
• good practice in scientific programming

Computational modelling
• building and using a computational model
• numerical simulation case studies, to include examples taken from elementary quantum
mechanics, cheminformatics, molecular dynamics / Monte Carlo, and/or inorganic chemistry.

Teaching Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion136:0036:00Project work
Scheduled Learning And Teaching ActivitiesLecture41:004:00Role of scientific computing in chemistry research
Guided Independent StudyDirected research and reading64:0024:00Preparation for coding and computer modelling dry labs
Scheduled Learning And Teaching ActivitiesWorkshops63:0018:00Coding and computer modelling dry labs
Guided Independent StudyIndependent study118:0018:00Independent study
Total100:00
Teaching Rationale And Relationship

Lectures discuss the use of scientific computing in chemistry research through case studies, thus motivating the real-world relevance of the material.

Students develop their skills in the python programming language through workshops, which take place in PC cluster rooms. Students prepare for the workshop through guided reading of the background material and the course textbook. Active learning, facilitated through Jupyter notebooks, is used to give students experience in solving chemical problems and building computational models through coding. Students use the notebooks to bring together discussions, equations, and interactive code in a single notebook environment.

Students work together in groups to emphasise the importance of well-documented, reproducible code and facilitate peer learning. Students will learn to develop computational models to solve common problems in elementary quantum mechanics, cheminformatics, molecular dynamics / Monte Carlo, and/or inorganic chemistry.

Assessment Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Report2M50Summary report on project work
Report2M50Lab notebook mark
Formative Assessments
Description Semester When Set Comment
Computer assessment2MAssessments to give students practice in coding, debugging and building computational models
Assessment Rationale And Relationship

A summary report on the project work will test understanding of the computational model, and the student's scientific computing and data analysis skills.

Assessment of the student's lab notebook tests their ability to write documented python code to solve problems in chemistry and to understand and report the outcomes of computational simulations.

Formative assessments will be set during each workshop, and will give students practice in coding, debugging, documenting, problem solving and building computational models. Feedback will be provided in class through peer review and at the end of each session through worked examples.

Reading Lists

Timetable