Advanced Materials And Nano Technology
The world around us is made of materials of various kinds and many of these are at the heart of great technological innovations. In recent times, with the development of nanotechnologies, the functionalities of conventional materials have advanced further and many novel applications are now being explored. Such advanced materials, both in the conventional (bulk) and nano form, are important in several fields such as energy conversion (solar cells) and storage (batteries), microelectronic devices, multiferroic materials, bio-compatible coatings and implants, high strength materials and functional materials for sensors, membranes etc. This interdisciplinary Dual Degree (ID-DD) program aims at equipping the students with an understanding of the fundamental science behind advanced materials and also training them with the practical tools and techniques of fabrication (materials and devices). The Department of Physics, IIT Madras is coordinating this new program with the active participation of several other departments.
Program Overview
The IDDD programme in Advanced Materials and Nanotechnology designed with inputs from Engineering and Science disciplines. Departments currently offering courses towards this program are Applied Mechanics, Chemical, Chemistry, Electrical, Materials and Metallurgy, Physics. It is typically coordinated by one of these departments. Currently it is coordinated by the Department of Physics.
Selection Process
B.Tech students (all branches) of IIT Madras can opt for this program. The minimum eligibility criteria prescribed by the senate is that the student should have a minimum of 8 CGPA at the end of 5 th semester.
Structure of the Program
The Curriculum is designed with the understanding that the modern field of Advanced materials and Nanotechnology is based on the exchange of ideas between sciences and Engineering branches.
The list of courses to be offered for students opting for DD in Advanced Materials and Nano Technology will have both core courses and electives. The students are allowed to choose four electives out of a total of 33 electives cutting across different disciplines. There will be four core courses that include a course on the Science and Technology of Solid State, two courses on nanomaterials and nanotechnology and a Laboratory course aimed at giving hands-on experience on Advanced materials and nano systems (36 credits of DD core course).
Those students who opt for the Dual Degree program will do the courses from their 7th Semester as prescribed below.
SEMESTER 7
| S No. | Course No. | Course Name | Credits |
| 1 | PH5011 | Core 1:Science and Technology of Solid State | 10 |
| 2 | PH6022 | Core 2:Introduction to Nanoscience | 9 |
SEMESTER 8
| S No. | Course No. | Course Name | Credits |
| 1 | PH6011 | Core 3: Nano materials and nanotechnology | 9 |
| 2 | Elective 1 | To be taken from the list of Electives mentioned | 9 |
| 3 | Elective 2 | To be taken from the list of Electives mentioned | 9 |
| 4 | PH6015 | Core 4: Advanced Materials and Nanotechnology Lab | 9 |
SEMESTER 9
| S No. | Course No. | Course Name | Credits |
| 1 | ID5190 | Project-I (Summer)/Summer internship | 25 |
| 2 | Elective 3 | To be taken from the list of Electives mentioned | 9 |
| 3 | Elective 4 | To be taken from the list of Electives mentioned | 9 |
| 4 | ID5191 | Project II (In the institute) | 20 |
SEMESTER 10
| S No. | Course No. | Course Name | Credits |
| 1 | ID5192 | Project III (in the Institute) | 40 |
View Curriculum View Department Electivies
Complex System and Dynamics
Complex systems consist of a large number of smaller entities that interact with each other usually in a nonlinear and stochastic manner. Examples of complex systems can be diverse including, but not limited to, coupled neurons in the brain, spatially distributed interacting systems like the movement of tectonic plates in a planet, transportation network of a large metropolitan, online social networks like Twitter and Facebook, and the ocean-atmosphere coupling in the climate system. Predicting phenomena such as brain epilepsy, climate change and global warming, pandemics, failure of networks involving distributed computing, power or transportation, species extinction in biological ecosystems, earthquakes, bio-mimetic flows and fluid-structure interactions, viral social media posts, forecasting financial crisis, crowd management etc.; these require a deeper understanding of the overall dynamics of the associated multi-physics systems and a complex system approach to investigate it. Mathematical models of such systems are complex, are typically nonlinear and stochastic and involve modeling the coupling between the interacting individual systems. The traditional reductionist approach that builds an understanding of the individual components, is not suitable for analyzing the collective behaviour. This necessitates the development of new techniques and approaches that have come to be known collectively as complex systems approach.
This is a rapidly emerging interdisciplinary field that enables bridging the gap between fundamental knowledge and mathematical models with the empirical observations as well as copious dataset available on account of the advent of new technological innovations that were unthinkable even a few years ago. The identification, organization and analysis of such data need to draw on the methods of data mining, artificial intelligence, neural nets and deep learning algorithms. The efficient use of this data in conjunction with the methods of probabilistic and causal analysis can lead to significant improvements in the construction of realistic models, as well as in the prediction of catastrophic events such as earthquakes, fluid structure interaction problems, power grid collapses, stock market collapses, and pandemics of disease that can occur in such systems.
The impact of these technical skills has the potential to directly address global challenges arising from human-nature interactions such as, climate change, pandemics, mass migration and ecological disorders, together with the possibility of harnessing technology towards growth and the improvement of life and the environment.
Program Overview
The aim of the proposed program is to introduce students to new techniques and tools for mathematical modelling and analysis of complex dynamical systems and to investigate some of the challenging dynamical problems in climate science, neuroscience, biological systems, Multiphysics systems and active flows, that are the focus of current research worldwide. In addition to enhancing the fundamental understanding of the universal features, which contribute to similar phenomena that occur across a diversity of systems, the effort could also translate into delivering technology which is useful in industrial and societal contexts.
Selection Process
B.Tech students (all branches) of IIT Madras can opt for this program. The minimum eligibility criteria prescribed by the senate is that the student should have a minimum of 8 CGPA at the end of 5 th semester.
Structure of the Program
The primary objectives are to train students
â— In building network based mathematical models for large scale complex dynamical systems using observational data and use the new theories of complex networks for analysis
â— In theories of nonlinear dynamical systems that enables analysis of complex systems which are inherently nonlinear
â— In high performance computing skills and data analysis
To achieve these objectives, the course curriculum consists of three compulsory core courses
(a) one from Complex Networks basket that introduces students to complex networks analysis
(b) one from Nonlinear Dynamics basket that exposes students on this subject
(c) one from a select set of Mathematics and Numerical Techniques courses. The courses have been selected to ensure that students can cater to be trained in appropriate skill sets that will be more suitable for their project.
The electives are to be selected from a basket of carefully curated courses that encompass the interdisciplinary nature of the program. The electives can be selected from courses on Data Science, High performance computing, as well as a mix of elective courses applicable to diverse engineering fields, which will enable the students to combine the traditional skills with the new approaches of complex dynamical systems.
SEMESTER 6
| S No. | Course No. | Course Name | Credits |
| 1 | Core 1 (any one) | Core Basket 1 | 12 |
| 2 | Core Basket 2 | 9 |
SEMESTER 7
| S No. | Course No. | Course Name | Credits |
| 1 | Core 2 (any one) | Core Basket 2 | 9 |
| 2 | Core Basket 1 | 12 |
SEMESTER 8
| S No. | Course No. | Course Name | Credits |
| 1 | Core 3 | Core Basket 3 | 9 |
Summer
| S No. | Course No. | Course Name | Credits |
| 1 | ID5890 | Project – I | 15 |
SEMESTER 9
| S No. | Course No. | Course Name | Credits |
| 1 | ID5891 | Project - II | 30 |
SEMESTER 10
| S No. | Course No. | Course Name | Credits |
| 1 | ID5892 | Project - III | 40 |
View Curriculum View Department Electivies
Quantum Science and Technology(QuST)
Quantum computing has heralded a fundamental technological shift, wherein the laws of quantum mechanics, characterized by superposition and entanglement, are being harnessed to perform computing tasks that would be otherwise intractable, even for supercomputers. Quantum science and technologies have taken giant strides over the last decade, with companies like IBM, Google and Honeywell showcasing quantum devices with 10-100s of quantum bits (qubits). Keeping in mind the widespread need for a quantum-skilled workforce, across academia and industry, IIT Madras introduced the interdisciplinary dual-degree program on quantum science and technologies (QuST) in 2020.
Program Overview
The core courses of the IDDD QuST programme are offered by faculty from the departments of Physics and Electrical Engineering., while the suggested elective courses are spread out across Physics, Electrical Engineering, Mathematics and Computer Science.
The aim of the program is to provide students with fundamental knowledge and skills in the domain of Quantum Science and Technology (QuST). Key learning outcomes include,
- Basic understanding of theoretical aspects of quantum bits, quantum gates, circuits and algorithms.
- Hardware skills: understanding the physical and technical aspects of building quantum devices, across different quantum computing architectures.
- Software skills: implementing quantum algorithms and protocols on existing quantum device platforms.
- Application of quantum information theory to fundamental physics questions in areas like many-body physics and cosmology.
Selection Process
B.Tech students (all branches) of IIT Madras can opt for this program. The minimum eligibility criteria prescribed by the senate is that the student should have a minimum of 8 CGPA at the end of 5 th semester.
Structure of the Program
IDDD-QuST has a very flexible curriculum. The programme spans a period of four semesters of the five-year dual degree programme. The core course on Quantum Computation and Quantum Information lays the required foundation for the program and the Quantum Computing Lab course being offered in partnership with IBM India, provides hands-on training in using the IBM quantum processors.
There will be five core courses that include a course on Quantum Computation and Quantum
Information, an ID course on Experimental Techniques for Quantum Computation and Metrology, a course on Quantum Electronics and Lasers, a course on Optical Signal Processing and Quantum Communications and the QC Lab course. (39 credits of DD core courses).
The core component also includes a project during the 9th and 10th semesters worth 85 credits.
SEMESTER 7
| S No. | Course No. | Course Name | Credits |
| 1 | PH5840 | Core 1: Quantum Computation and Quantum Information | 9 |
| 2 | EE4348 | Core 2: Quantum Electronics and Lasers (Includes a 3-credit lab component) | 12 |
SEMESTER 8
| S No. | Course No. | Course Name | Credits |
| 1 | ID5843 | Experimental Techniques for Quantum Computation and Metrology | 9 |
| 2 | EE6502 | Optical Signal Processing and Quantum Communications | 9 |
| 3 | Elective I | To be selected from the given list of courses | 9 |
| 4 | Elective II | To be selected from the given list of courses | 9 |
SEMESTER 9
| S No. | Course No. | Course Name | Credits |
| 1 | ID5790 | Project - I (Summer) | 25 |
| 2 | ID5791 | Project – II | 20 |
| 3 | Elective III | To be selected from the given list of courses | 9 |
| 4 | Elective IV | To be selected from the given list of courses | 9 |
SEMESTER 10
| S No. | Course No. | Course Name | Credits |
| 1 | ID5792 | Project – III | 40 |