Large Language Model-Assisted Computational Material Science
About This Course
This course aims to equip learners with advanced knowledge and practical skills at the intersection of materials science, computational simulation, and artificial intelligence (AI), enabling learners to understand, model, and solve complex materials problems using modern AI-assisted approaches. By taking this course, learners will gain hands-on experience with Molecular Dynamics simulations, Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), and the integration of Large Language Models and Retrieval-Augmented Generation techniques to build end-to-end computational pipelines. These skills will prepare learners for advanced research projects, Doctorate studies, and careers in industries such as advanced materials, energy, electronics, and manufacturing, where AI-driven computational modelling is increasingly essential for innovation and decision-making.
As this micro-credential course is only offered in the FlexiMasters in Computational Chemical Modelling and AI programme, learners are required to enrol into the programme and complete all required courses within this programme.
What You'll Learn
• Implement Molecular Dynamics simulations using Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) to investigate structure–property relationships in materials systems.
• Analyze material structures and properties at the atomic scale using theoretical principles and computational representations.
• Integrate Large Language Models into computational materials science workflows to automate simulation setup, analysis, and optimization tasks.
• Design end-to-end Large Language Models and Retrieval-Augmented Generation (LLM–RAG) enabled computational pipelines for solving complex materials science research problems.
Entry Requirements
No pre-requisites are needed to enroll in the individual courses; however, learners without qualifications or work experience in relevant engineering or the related fields may find the course contents challenging.
Note: Shortlisting will be conducted for the courses