Outline

Overview

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.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Understanding of basic principles of Newtonian mechanics, physics and chemistry, fluid mechanics and solid mechanics
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Ahmad Jabbarzadeh, ahmad.jabbarzadeh@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Assignment 1 Written answers to theoretical and computational problems.	20%	Week 04 Due date: 25 Aug 2023 at 23:59 Closing date: 01 Sep 2023	10-20 pages.
Outcomes assessed:
Assignment	Assignment 2 A written report of the simulation results for computational problems.	20%	Week 08 Due date: 22 Sep 2023 at 23:59 Closing date: 29 Sep 2023	10-20 pages.
Outcomes assessed:
Assignment	Project Conducted over 6.5 weeks to produce a substantial report on the findings.	50%	Week 13 Due date: 03 Nov 2023 at 23:59 Closing date: 10 Nov 2023	40-75 Pages.
Outcomes assessed:
Presentation	Presentation/seminar The Seminar will report the Major Project results.	10%	Week 13 Due date: 30 Oct 2023 at 23:59 Closing date: 03 Nov 2023	10-20 PowerPoint slides;12 min+ 3min Q/A
Outcomes assessed:
= group assignment ?

Assessment summary

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	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

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.

Academic integrity

The Current Student website provides information on academic integrity 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 integrity breaches seriously.

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

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
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)
Week 01	Introduction to modelling and simulation; 2. Continuum and atomic view of the matter	Lecture (2 hr)
Week 02	Deterministic versus stochastic methods; 2. Particle methods, 3. Molecular dynamics algorithms	Lecture and tutorial (5 hr)
Week 03	Molecular dynamics algorithms and techniques	Lecture and tutorial (5 hr)
Week 04	Calculating physical properties using statistical mechanics, Temperature, Pressure, rheological properties	Lecture and tutorial (5 hr)
Week 05	Error Analysis, Radial Distribution Function, Calculation of Local Properties, other Potentials, and, Temperature control	Lecture and tutorial (5 hr)
Week 06	Starting Configurations, Energy Minimization, Visualization, NVT and NPT Ensembles	Lecture and tutorial (5 hr)
Week 07	Efficient computations, Parallel Processing	Lecture and tutorial (5 hr)
Week 08	Coarse Graining Methods	Lecture and tutorial (5 hr)
Week 09	Modelling polymers, phase transitions, and nanofluidics	Lecture and tutorial (5 hr)
Week 10	Time dependent phenomena, modeling nano- mechanics- tribological contacts	Lecture and tutorial (5 hr)
Week 11	Practical applications - Case studies simulation of flow in nanochannels, shear flow, nanotribology and nano-rheology	Lecture and tutorial (5 hr)
Week 12	Practical applications - Case studies	Lecture and tutorial (5 hr)
Week 13	Major Project Presentations	Lecture and tutorial (5 hr)

Attendance and class requirements

The lectures are recorded, so if you are missing classes, please watch the recorded lectures. Attending tutorials are very important in developing practical skills in utilizing various software used in this unit of study. Therefore, following 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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

I am pleased with the excellent satisfaction rating (%96) and encouraging comments from the students who participated in this Unit. The Unit was on Dean's Commendation List in 2022, following several achievements in previous years. This is a relatively small unit of study, allowing a better engagement with the students in this class. Most students who completed this Unit showed strong commitment and achieved good results. Students liked many aspects of the course and cited "informative and innovative" content and the "unique" nature of this Unit, learning "new technology and data processing skills", working with advanced computational systems and software, and engaging tutorials, case studies, and projects. They also suggested on-campus in-person attendance. We will have all learning activities in-person with all students back on campus. Also, 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 the possibility of doing an honours Thesis/Capstone Project/Dissertation on the topic. I wish you all the best for the rest of your studies.

Additional information

Please check Canvas site for additional information.

Site visit guidelines

N/A

Work, health and safety

N/A

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

AMME5271: Computational Nanotechnology

Semester 2, 2023 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect