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CHY1610 : Introduction to Scientific Computing for Chemists

  • Offered for Year: 2022/23
  • Module Leader(s): Dr James Dawson
  • Lecturer: Professor Thomas Penfold, Dr Agnieszka Bronowska
  • Owning School: Natural and Environmental Sciences
  • Teaching Location: Newcastle City Campus
Semester 2 Credit Value: 10
ECTS Credits: 5.0


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

Teaching Activities
Category Activity Number Length Student Hours Comment
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 ActivitiesWorkshops63:0018:00PiP if possible, or Synch online. Coding & problem solving on Zoom. Scheduled on Weds
Scheduled Learning And Teaching ActivitiesDrop-in/surgery111:0011:00Office hour drop in sessions
Guided Independent StudyIndependent study123:0023:00Background reading
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

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

Other Assessment
Description Semester When Set Percentage Comment
Report2M100Project Report (max 5 pages excluding code)
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.

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 2 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