CHEM ENG 7102 - Computation for Materials Engineering

North Terrace Campus - Semester 2 - 2019

This course provides an introduction to modelling and simulation of materials, covering force-field based atomistic and molecular simulation, and first principle calculation based on quantum mechanics. Students will learn how to use computation as new tools to predict functional material properties. Students will also learn how to design materials and structures from the bottom up - to make lighter, stronger, more efficient, and less expensive materials. In this subject, students will get hands-on training in both the fundamentals and applications of these exciting new methods to key engineering problems, such as energy, optical, and quantum. On successful completion of this course students will be able to: - Demonstrate knowledge of theories for computation, such as force field methods and quantum mechanics. - Demonstrate knowledge of modern computation techniques for materials engineering; - Understand the capability and limitation of computation techniques for materials engineering; - Understand the architecture of high-performance-computing (HPC) facilities; - Apply computation techniques to model specific chemical and physical properties of materials for energy, optical, or quantum applications; - Apply basic scripting to facilitate and accelerate the modelling of materials.

  • General Course Information
    Course Details
    Course Code CHEM ENG 7102
    Course Computation for Materials Engineering
    Coordinating Unit School of Chemical Engineering
    Term Semester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 6 hours per week
    Available for Study Abroad and Exchange Y
    Course Description This course provides an introduction to modelling and simulation of materials, covering force-field based atomistic and molecular simulation, and first principle calculation based on quantum mechanics. Students will learn how to use computation as new tools to predict functional material properties. Students will also learn how to design materials and structures from the bottom up - to make lighter, stronger, more efficient, and less expensive materials. In this subject, students will get hands-on training in both the fundamentals and applications of these exciting new methods to key engineering problems, such as energy, optical, and quantum.
    On successful completion of this course students will be able to:
    - Demonstrate knowledge of theories for computation, such as force field methods and quantum mechanics.
    - Demonstrate knowledge of modern computation techniques for materials engineering;
    - Understand the capability and limitation of computation techniques for materials engineering;
    - Understand the architecture of high-performance-computing (HPC) facilities;
    - Apply computation techniques to model specific chemical and physical properties of materials for energy, optical, or quantum applications;
    - Apply basic scripting to facilitate and accelerate the modelling of materials.
    Course Staff

    Course Coordinator: Dr Yan Jiao

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    1. Demonstrate knowledge of theories for computation, such as force field methods and quantum mechanics.
    2. Demonstrate knowledge of modern computation techniques for materials engineering;
    3. Understand the capability and limitation of computation techniques for materials engineering;
    4. Understand the architecture of high-performance-computing (HPC) facilities;
    5. Apply computation techniques to model specific chemical and physical properties of materials for energy, optical, or quantum applications;
    6. Apply basic scripting to facilitate and accelerate the modelling of materials.
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    1,2,4
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    3,5,6
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    5,6
  • Learning Resources
    Recommended Resources
    Reference Books

    Computer Simulation of Liquids: Second Edition. by Michael P. Allen and Dominic J. Tildesley
    Essentials of Computational Chemistry: Theories and Models 2nd Edition, by Christopher J. Cramer
    Online Learning
    https://myuni.adelaide.edu.au/enroll/MW9AN9
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Discussion and hands-on based interactive lectures; problem-solving based practicals.
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity Contact Hours Workload Hours
    Lectures 24 48
    Practicals 22 44
    Total 46 92
    Learning Activities Summary

    No information currently available.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Task Type Due* Weighting Learning Outcome
    Essays on selected topics Summative

    Weeks 2-12

    20% 1,2,3
    Computation Projects Summative Weeks 2-12 40% 1,2,4,5,6
    Final Exam Formative 40% 1,2,3,4

    * The specific due date for each assessment task will be available on MyUni.
    Assessment Detail

    No information currently available.

    Submission

    No information currently available.

    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
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