Artificial Intelligence and Machine Learning in Materials Engineering

By Prof. Krishanu Biswas | IIT Kanpur

Learners enrolled: 8173 | Exam registration: 882

ABOUT THE COURSE:

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.

INTENDED AUDIENCE: UG, PG from Academic Institutes and Industry Professionals from various Industries

PREREQUISITES: Basic concept on Materials Science

INDUSTRY SUPPORT: YES, Tata Steels, TRDDC, Jindal, Hidulco

Summary

Course Status :	Completed
Course Type :	Elective
Language for course content :	English
Duration :	12 weeks
Category :	Metallurgy and Material science & Mining Engineering Minor in Metallurgy
Credit Points :	3
Level :	Undergraduate/Postgraduate
Start Date :	22 Jul 2024
End Date :	11 Oct 2024
Enrollment Ends :	05 Aug 2024
Exam Registration Ends :	16 Aug 2024
Exam Date :	02 Nov 2024 IST

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1 :

Introduction to the course: This will provide basic aspects of the course: why AI/ML in Materials Engineering.
Basics of Materials Science-I: Structure of Materials
Basics of Materials Science-II: Microstructure –property correlation

Week 2 :

Basics of Materials Science-III: Processing of materials
Basics of Materials Science-IV: Correlation between processing with materials structure-I
Basics of Materials Science-V: Correlation between processing with materials structure-II

Week 3 :

Materialsdesign at different length scale - I: Basic Design Principles
Materialsdesign at different length scale - II: Component level to atomic level aspects of material design
Materialsdesign at different length scale - III: CALPHAD;

Week 4 :

Materialsdesign at different length scale - IV: Ab initio, Density Functional Theory (DFT),
Materialsdesign at different length scale - V: Monte Carlo (MC) or Molecular Dynamic (MD) followed Phase Field Simulations (PFM) for microstructural evolution.
Machine Learning Approaches for Materials Design-I: Statistical Tools, Machine Learning-I

Week 5 :

Machine Learning Approaches for Materials Design-II: Statistical Tools, Machine Learning,-II
Machine Learning Approaches for Materials Design-III: Statistical Tools, Machine Learning-III
Machine Learning Approaches for Materials Design-IV: Computer vision-I
Machine Learning Approaches for Materials Design-V: Computer vision-II

Week 6 :

Machine Learning Approaches for Materials Design-VI: Microstructural evolution-I
Machine Learning Approaches for Materials Design-VII: Microstructural evolution-II
Machine Learning Approaches for Materials Design-VIII: Microstructure property correlation-I

Week 7 :

Machine Learning Approaches for Materials Design-IX: Microstructure property correlation-II
Machine Learning Approaches for Materials Design-X: Microstructure property correlation-III
Accelerating Materials Development and Deployment-I: Microstructure property correlation-IV

Week 8 :

Accelerating Materials Development and Deployment-II: Deep Learning-I
Accelerating Materials Development and Deployment-III: Deep Learning-II
Accelerating Materials Development and Deployment-IV: Deep Learning-III

Week 9 :

Accelerating Materials Development and Deployment-V: Inverse design using AI/ML – from evolutionary algorithms to deep learning-I
Materials Knowledge and Materials Data Science-I: Inverse design using AI/ML – from evolutionary algorithms to deep learning-II
Materials Knowledge and Materials Data Science-II: Inverse design using AI/ML – from evolutionary algorithms to deep learning-III

Week 10 :

Materials Knowledge and Materials Data Science-III: Advanced Deep Learning-I
Materials Knowledge and Materials Data Science-IV: Advanced Deep Learning-I
Materials Knowledge and Materials Data Science-V: AI/ML for materials characterization-I
Materials Knowledge and Materials Data Science-VI: AI/ML for materials characterization-II

Week 11 :

Materials Knowledge and Materials Data Science-VII: AI/ML for materials characterization-III
Materials Knowledge and Materials Data Science-VIII: AI/ML for materials characterization-IV
Materials Knowledge and Materials Data Science-VIII: AI/ML for autonomous experiments-I
Materials Knowledge and Materials Data Science-VIII: AI/ML for autonomous experiments-II

Week 12 :

Materials Knowledge and Materials Data Science-IX: Materials Informatics and Data Science-I
Materials Knowledge and Materials Data Science-IX: Materials Informatics and Data Science-II
Materials Knowledge and Materials Data Science-IX: Materials Informatics and Data Science-III
Materials Knowledge and Materials Data Science-X: Summary and Way forward

Books and references

T. Lookman, Stephan Eidenbennz, et al., Materials Discovery and Design by Means of Data Science and Optimal Learning, Springer , 2018
Y. Cheng, T. Wang ad G. Zhang; Artificial Intelligence for Materials Science, Springer ,2018
Phil Del Luna, Accelerated Materials Discovery, De Grutyer, 2022

Instructor bio

Prof. Krishanu Biswas

IIT Kanpur

Prof. Krishanu Biswas, Ranjit Singh chair professor at the Department of Materials Science and Engineering at IIT Kanpur. He is a prolific teacher, developed courses on Phase Diagrams, Phase Transformation and Nanomaterials under the umbrella of NPTEL. He teaches both UG/PG courses at IIT Kanpur. He also works on application of AI-ML in materials engineering. His research includes multicomponent materials, materials for hydrogen energy, electron microscopy ete.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 02 November 2024 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Kanpur .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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