NES2406 : Scientific Computing for Chemists
NES2406 : Scientific Computing for Chemists
- Offered for Year: 2024/25
- Module Leader(s): Dr Daniel Cole
- Owning School: Natural and Environmental Sciences
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
N/A
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
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
• object-oriented programming
• python libraries for scientific computing
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.
Learning Outcomes
Intended Knowledge Outcomes
At the end of the module a student will be able to:
• Discuss the wider role of scientific computing in chemistry research;
• Employ and design python code for chemistry problem solving;
• Understand the role of good practice in scientific programming;
• Plan and construct computational models in a range of chemistry disciplines.
Intended Skill Outcomes
At the end of the module a student will be able to:
• Use the Jupyter notebook for interactive computing;
• Develop well documented python code;
• Employ python libraries to increase efficiency of code;
• Apply equations and chemical principles to computational problem solving;
• Plan, run and report the results of computational simulations.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 4 | 1:00 | 4:00 | Role of scientific computing in chemistry research |
| Guided Independent Study | Assessment preparation and completion | 1 | 36:00 | 36:00 | Project work |
| Guided Independent Study | Directed research and reading | 6 | 4:00 | 24:00 | Preparation for coding and computer modelling dry labs |
| Scheduled Learning And Teaching Activities | Workshops | 6 | 3:00 | 18:00 | Coding and computer modelling dry labs |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 11 | 1:00 | 11:00 | Office hour drop in sessions |
| Guided Independent Study | Independent study | 1 | 7:00 | 7:00 | Independent study |
| Total | 100:00 |
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.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 2 | M | 100 | Summary report on project work |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Computer assessment | 2 | M | Assessments 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.
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.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- NES2406's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- NES2406's past Exam Papers
General Notes
N/A
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The information contained within the Module Catalogue relates to the 2024 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2025/26 entry will be published here in early-April 2025. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.