Teaching

MSE 301: Phase Transformation of Materials

NOC: Phase Transformation in Materials

The present course will deal with the basics of phase transformation in materials. Using thermodynamics, kinetics of phase transformation, different liquid to solid and solid to solid transformations will be covered in this course.

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NOC: Deep Learning in Material Science and Engineering: From Concepts to Applications

Why Deep Learning in MSE? Deep Learning has widely been used in various sectors; health care, finance, agriculture, education, energy, entertainment etc have been using Deep Learning to improve their performances. Tech giants have been pushing their boundaries to achieve more as Deep Learning is the driving force everywhere and similarly the demand for skilled professional has steeply been rising. The design and discovery of new materials, processing optimization and prediction of properties would require applications of Deep Learning techniques and even deployment of the same. The present course is meant for students preparing for higher studies, researchers applying AI tools, the industry professionals wanting to upskill. This is a balanced course with conceptual learning and hands-on with ability to build intuition, apply concepts to data, and interpret model output. Upon successfully completing this course, you will have a mini portfolio of Deep Learning projects, which can be showcased to the po tential employers or use for research.

NOC: Nanomaterials and their Properties

The present course will deal with the basics of phase transformation in materials. Using thermodynamics, kinetics of phase transformation, different liquid to solid and solid to solid transformations will be covered in this course.

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NOC: Artificial Intelligence and Machine Learning in Materials Engineering

Artificial intelligence (AI) has taken the center-stage of material development due to rapid increase of the computational power and speed. The use of AI is rapidly picking up for high throughput screening and decision making for materials chemistry, property estimation and optimization of properties. As the need of new materials with improved properties is felt across the discipline, the accelerated design and development has become a key aspect of material science and engineering. The the need for use of AI and machine learning has widely been felt to design new materials with better properties The course is intended to provide this some basics aspects of use of AI and ML in materials science and engineering. Starting with processing -structure-property correlation, the basic computational tools will be deliberated with application of machine learning and deep learning. The application of AI-ML will be discussed with some case studies and examples.

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NOC: Advanced Characterization Techniquesg (Dr. Krishanu Biswas, Prof.N.P.Gurao)

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NOC: Phase Diagrams in Materials Science and Engineering

Phase diagrams are important for materials science and engineering applications encompassing from structural materials to functional materials, including electronic, magnetic applications. The course is intended to make the students and research familiarize with binary and ternary phase diagrams and microstructure of different materials. It is to be noted that microstructure plays a vital role in deciding the properties of the materials. Thus, it is important to connect the phase diagram information for microstructural evolution

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NOC: Introductory Materials Informatics (Prof. Krishanu Biswas, Prof. Pratik Kumar Ray, IIT Ropar)

Material informatics is transforming the way materials are discovered, designed, developed, selected, and deployed. Use of informatics/data science helps make better decisions by providing insights into risks, patterns, and trends. In this course, we will learn about the impact of data science on materials engineering with emphasis on Materials Informatics. Although computer models of materials are widely used, but there is an increasing trend to data driven approaches. This data might be derived from high throughput computations as well as experiments. This is course is intended to provide knowledge on materials design, discovery with applications of modern material informatics tools and large-scale multiscale modeling—with the ultimate goal to speed up the design process and implement cost effective rapid discovery, prototyping and elucidating the decision-making process.

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