Module: Practical Chemistry with Artificial Intelligence

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Practical Chemistry with Artificial Intelligence
Module Code	CHM1031 (ITS: CS320)
Faculty	Chemical Sciences	School	Science & Health
NFQ level	8	Credit Rating	10

Description

In this module, students are introduced to the practical application of artificial intelligence in chemistry. This includes a blend of wet labs and virtual simulations in pharmaceutical and analytical chemistry, and use of commercial software packages for prediction of chemical properties. Students will revise and extend their understanding of calculus, in particular functions multiple variables, which underpin principals in machine learning.

Learning Outcomes

1. Gain practical hands-on experience operating analytical instrumentation and analysing samples using quantitative instrumental techniques [Learning outcome for wet labs]
2. Collect, process, visualise and interpret experimentally-generated and simulated quantitative and qualitative data [Learning outcome for wet and virtual labs]
3. Gain practical and management skills for industry-readiness
4. Prepare high quality scientific reports on quantitative and qualitative data
5. Use commercial artificial intelligence packages to investigate properties of molecules and materials, developing predictions for drug design or materials applications.
6. Perform basic operations on vectors, interpret and represent functions of more than one variable, calculate partial derivatives and gradients of functions of more than one variable, and find critical points and solve optimisation problems. Students will be able to demonstrate the ability to carry out these procedures and analyse the outcomes.
7. Communicate effectively in oral formats, adapting to audience profile as required (communicative competence).
8. Demonstrate collaborative communication in a team environment (teamwork and collaboration).

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	12	Mathematics minimodules delivered in a hybrid teaching model with asynchronous online sessions, supplemented by live tutorials
Assignment Completion	13	Preparation for formative in-class and summative end of semester assessments for mathematics minimodule
Workshop	27	Use of commercial software package to quantify and predict chemical properties, identify trends and to predict properties of drug-like modules or materials.
Group work	15	Group minichallenge using commercial software package to predict properties of drug-like molecules
Assignment Completion	10	Preparation of final presentation
Laboratory	54	Wet lab and virtual experiments in analytical and pharmaceutical chemistry
Directed learning	24	Students will be provided with recommended reading and multimedia resources (through Loop page)
Assignment Completion	48	Preparation of reports of findings from wet labs
Independent Study	47	Revision of past courses and how they relate to lecture materials for this module, identifying where concepts from this course are related to other modules, preparation for in-class test
Total Workload: 250

Section Breakdown
CRN	10260	Part of Term	Semester 1
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC2	Best Mark	N
Module Co-ordinator	Emma Coyle	Module Teacher	Loanda Cumba, Mary Pryce

Type	Description	% of total	Assessment Date
Assessment Breakdown
Practical/skills evaluation	Activities in the laboratory - evaluation of laboratory skill, safety, use of apparatus and other practical skills for evaluation	25%	As required
Laboratory Portfolio	Write up findings from labs in analytical and pharmaceutical chemistry 5% org 10% cert of analysis 10% VL 15% Analytical experiments	40%	As required
Practice assessment record	Engagement with commercial software to investigate application of AI (black box approach)	0%	Every Week
Presentation	Students will prepare a summary of their chemistry with AI challenge, including discussion of their inputs and outputs, potential limitations to the software package used, and ways to overcome these challenges.	25%	Sem 1 End
Completion of online activity	Complete formative H5P quizzes in mathematics micromodules	0%	Every Second Week
Written Exam	End of term quiz on maths micromodules	10%	Sem 1 End

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Experiments performed in the Analytical part:
1. Spectroscopic Determination of Iron in Iron Tablets. 2. Comparison studies of analytical methods: Magnesium in pharmaceutical preparations by Atomic Absorption and Titrimetry. 3. Chromatography (HPLC or GC)

Experiments performed in the Pharmaceutical part:
1. Synthesis and analysis of the active pharmaceutical ingredient 2. Testing of raw materials used in the manufacture of the Panadol 500mg Tablets. 3. Isoxazoline syntheis (1,3-dipolar reaction)

Virtual laboratories
VL 1: HPLC

VL2: Ion chromatography

Commercial software
Guided tutorials in

1. Chemical bonding

2. VSEPR Theory

3. HOMO-LUMO Energy Gap

4. SN2 reactions

5. Nucleophiles and Electrophiles

6. Stereoisomers

7. IR Spectroscopy

8. Diels-Alder reactions

followed by a mini challenge in drug design

Mathematics micromodules
1. Review of vectors

2. Coordinate Geometry

3. Functions of more than one variable

4. Derivatives of functions of more than one variable

5. Critical points of functions of more than one variable

6. Distance functions/metrics

Indicative Reading List

Books:

DCU Chemical Sciences: 2023, Combined laboratory manual - AI3, Chemical Sciences, DCU, 1,

Articles:
None

Other Resources

None