Skip to main content

Module

CHY1610 : Introduction to Scientific Computing for Chemists

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

Aims

This module aims to introduce the role of scientific computing in chemistry to students and familiarise them with python as an example of a programming language. Students will also be introduced to computational modelling and simulations

Outline Of Syllabus

The role of scientific computing in chemistry:
• introduction and case studies

Introduction to the python coding language:
• numbers
• variables
• loops
• logic
• functions
• working with data files

Use of python modules and packages:
• plotting data
• in-built functions

Aspects of computational chemistry:
• building a computational model
• numerical simulations
• convergence and accuracy

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
Scheduled Learning And Teaching ActivitiesLecture21:002:00Lectures are non-synchronous online. Introduce course and provide case studies.
Guided Independent StudyAssessment preparation and completion124:0024:00Writing up report
Guided Independent StudyDirected research and reading64:0024:00Preparation for coding and problem solving workshops
Scheduled Learning And Teaching ActivitiesWorkshops63:0018:00Synch online. Coding & problem solving on Zoom. Note these can be scheduled on Weds to avoid a clash
Guided Independent StudyIndependent study132:0032:00Background reading
Total100:00
Teaching Rationale And Relationship

Online Seminars introduce the use of scientific computing in chemistry research through case studies, thus motivating the real-world relevance of the material, and give information on how to access further online resources and documentation.

Students are introduced to python as an example of a programming language through online workshops. 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 through coding. Students learn to bring together text, equations, and interactive code in a single notebook environment, allowing them to actively engage with the course content.

Students work together in groups to reinforce the importance of well-documented, reproducible code and facilitate peer learning. Students will be introduced to computational models through examples from the scientific literature. Fundamental concepts in computational chemistry, such as accuracy and convergence, will be investigated through interacting with the inputs to these models.

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
Report2M50Project Report.
Report2M50Lab notebook mark
Formative Assessments
Description Semester When Set Comment
Computer assessment2MFormative assessment set during workshops
Assessment Rationale And Relationship

A short project report tests the student’s ability to set up and run a simulation and report on convergence and accuracy.

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

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


** Students studying from abroad may request to take their exam before the semester 1 exam period, in which case the format of the paper may differ from that shown in the MOF. These students should contact the school to discuss this **

Reading Lists

Timetable