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Unit of study_

AMME5271: Computational Nanotechnology

This course introduces atomistic computational techniques used in modern engineering to understand phenomena and predict material properties, behaviour, structure and interactions at nano-scale. The advancement of nanotechnology and manipulation of matter at the molecular level have provided ways for developing new materials with desired properties. The miniaturisation at the nanometre scale requires an understanding of material behaviour which could be much different from that of the bulk. Computational nanotechnology plays a growingly important role in understanding mechanical properties at such a small scale. The aim is to demonstrate how atomistic level simulations can be used to predict the properties of matter under various conditions of load, deformation and flow. The course covers areas mainly related to fluid as well as solid properties, whereas, the methodologies learned can be applied to diverse areas in nanotechnology such as, liquid-solid interfaces, surface engineering, nanorheology, nanotribology and biological systems. This is a course with a modern perspective for engineers who wish to keep abreast with advanced computational tools for material characterisation at the atomic scale.


Academic unit Aerospace, Mechanical and Mechatronic
Unit code AMME5271
Unit name Computational Nanotechnology
Session, year
Semester 2, 2021
Attendance mode Normal day
Location Remote
Credit points 6

Enrolment rules

Assumed knowledge

Understanding of basic principles of Newtonian mechanics, physics and chemistry, fluid mechanics and solid mechanics.

Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Ahmad Jabbarzadeh,
Lecturer(s) Ahmad Jabbarzadeh ,
Tutor(s) Ahmad Jabbarzadeh ,
Type Description Weight Due Length
Assignment Assignment 1
Written answers to theoretical and computational problems.
20% Week 04 10-20 pages.
Outcomes assessed: LO1 LO3 LO4 LO5
Assignment Assignment 2
A written report of the simulation results for computational problems.
20% Week 08 10-20 pages.
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Project
Conducted over 6.5 weeks to produce a substantial report on the findings.
50% Week 13 40-75 Pages.
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Presentation group assignment Presentation/seminar
The Seminar will report the Major Project results.
10% Week 13 10-20 PowerPoint slides;12 min+ 3min Q/A
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
group assignment = group assignment ?


  • Assignment 1                                                       20%        (week 4)
  • Assignment 2                                                       20%        (week 8)
  • Major Project                                                     50%        (week 13)
  • Seminar Presentation:                                      10%        (week 13)


  • Assignment 1: Written solutions to the theoretical and computational problems, should be prepared and submitted via Canvas. 
  • Assignment 2: This is a computational task and the assignment should report the results of computational nanotechnology simulation problems. Students should independently conduct the simulations, post-process, analyse the results and write the report and submit via Canvas.
  • Major Project: A major computational problem that should be conducted over 6.5 weeks. The simulations should be done independently, and a substantial report should be prepared to present the results and discuss the findings.
  • Presentation/seminar: In the last week of the semester the students should give seminars on the topic of their major project. All the presentations should be in PowerPoint format and all members of the group should participate. (10-12 minutes presention+ 3 min Q/A)

Detailed information for each assessment can be found on Canvas.


Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late submission of Assignment 1, 2, Major Project, and Presentation Seminar will be penalized 5% per day including weekend days.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Multiple weeks The students are expected to spend 7 hours per week during the semester on an independent study that includes, completion of tutorial activities, reviewing the lectures, tutorial and online resources, reading the textbooks in the reading lists, learning the various software used for doing the assignments and project, and conducting own research in the library and other resources. Independent study (91 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 01 1. Introduction to modelling and simulation; 2. Continuum and atomic view of the matter Lecture (2 hr) LO4
Week 02 1. Deterministic versus stochastic methods; 2. Particle methods, 3. Molecular dynamics algorithms Lecture and tutorial (5 hr) LO2 LO4 LO5
Week 03 1. Molecular dynamics algorithms and techniques Lecture and tutorial (5 hr) LO2 LO3 LO4 LO5
Week 04 Calculating physical properties using statistical mechanics, Temperature, Pressure, rheological properties Lecture and tutorial (5 hr) LO3 LO4 LO5
Week 05 Error Analysis, Radial Distribution Function, Calculation of Local Properties, other Potentials, and, Temperature control Lecture and tutorial (5 hr) LO1 LO3 LO4 LO5
Week 06 Starting Configurations, Energy Minimization, Visualization, NVT and NPT Ensembles Lecture and tutorial (5 hr) LO1 LO3 LO4 LO5
Week 07 Efficient computations, Parallel Processing Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5
Week 08 Coarse Graining Methods Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 09 Modelling polymers, phase transitions, and nanofluidics Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 10 Time dependent phenomena, modeling nano- mechanics- tribological contacts Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 11 Practical applications - Case studies simulation of flow in nanochannels, shear flow, nanotribology and nano-rheology Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 12 Practical applications - Case studies Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 13 Major Project Presentations Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7

Attendance and class requirements

The lectures are recorded, so if you are missing lectures, please watch the recorded lectures. Attending tutorials are very important in developing practical skills in utilizing various software used in this unit of study. While tutorials are recorded when the material is presented to the entire class, individual face-to-face interactions are not recorded. Therefore, attending the tutorials will maximize the benefits and learning experience.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Please see the course eReading on Canvas

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. calculate properties of materials such as simple fluids and polymer melts, solids, explore structure-property relations in various situations
  • LO2. understand specific processes stated in the aims and goals, and apply it to specific problems
  • LO3. relate the microscopic state of materials to their macroscopic properties such as stresses, temperature, strain and viscosity. They will learn how to set up simulations of materials and probe their properties, interpret the results from visualised molecular snapshots
  • LO4. understand basic and advanced theory of molecular dynamics simulation techniques such as force potentials for modelling fluids and solids, statistical analysis and accuracy, and advanced algorithms of high performance computations
  • LO5. be familiar with available scientific software for computational nanotechnology and will learn how to use software and conduct their projects
  • LO6. prepare reports and present their findings in a professional manner
  • LO7. work within a group to conduct research and share work load to achieve common objectives.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
I am pleased with the excellent satisfaction rating and encouraging comments and kind words from the students who participated in this unit. The course has been on Dean’s Commendation list several times. Students cited working with advanced computational systems and software as those they liked most. In discussions with you, some students expressed a desire to learn more about computational nanotechnology. For those students who want to learn more, I will be happy to discuss possibilities to do honours thesis/Capstone Project/Dissertation on this topic for those students who want to learn more. The unit was delivered for 12 weeks in 2020. In 2021, we will deliver this unit over 13 weeks, and it will be fully online.

Please check Canvas site for additional information.

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