| Course Name | Introduction to Materials Informatics |
| Course Code | 23CHY115 |
| Program | B.Tech in Artificial Intelligence and Data Science |
| Semester | 2 |
| Credits | 3 |
| Campus | Coimbatore , Amritapuri ,Faridabad , Bangaluru, Amaravati |
Introduction to material science – structure, properties, and process spaces – process-structure-property linkages – foundation of material informatics – introduction to molecular mechanism and force field – quantification of dynamics properties of polymers (monte carlo simulation, molecular dynamics simulation, normal mode analysis) – electronics structure of atoms (Gaussian, Gauss view, density functional theory)
Quantification and screening of materials properties – property prediction and optimization using AI – materials design and discovery using AI – how to handle small, spared, and low-quality dataset using AI
Materials failure and sustainability analysis – new material and inverse materials design concept – solve inverse design using AI – enhance speed, efficacy and cost-effectiveness of material using AI – basic concept of quantum computing in material informatics.
Case studies of materials informatics (use of AI) in different fields (e.g. energy, aerospace, biomedical, etc.) – ethical considerations and limitations of materials informatics – future directions and challenges in materials informatics.
Course Objectives
Course Outcomes
After completing this course, students will be able to
|
CO1 |
Apply modern material informatics tools including machine learning, simulations. Modelling, visualizations to solve a specific challenge most significantly. |
|
CO2 |
Analysis material failure and sustainability |
|
CO3 |
Design new materials and solve inverse design problem using AI. |
|
CO4 |
Developed efficient predictive model using small, spares and low-quality dataset. |
CO-PO Mapping
|
PO/PSO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
PSO1 |
PSO2 |
PSO3 |
|
CO |
|||||||||||||||
|
CO1 |
3 |
3 |
2 |
3 |
3 |
2 |
– |
– |
2 |
2 |
1 |
2 |
– |
3 |
3 |
|
CO2 |
3 |
3 |
1 |
3 |
3 |
2 |
2 |
1 |
2 |
3 |
2 |
2 |
– |
3 |
– |
|
CO3 |
3 |
3 |
3 |
3 |
3 |
2 |
2 |
1 |
3 |
3 |
2 |
2 |
3 |
3 |
3 |
|
CO4 |
3 |
3 |
3 |
3 |
3 |
2 |
2 |
1 |
3 |
3 |
2 |
2 |
3 |
2 |
3 |
Evaluation Pattern
|
Assessment |
Internal/External |
Weightage (%) |
|
Assignments (minimum 2) |
Internal |
30 |
|
Quizzes (minimum 2) |
Internal |
20 |
|
Mid-Term Examination |
Internal |
20 |
|
Term Project/ End Semester Examination |
External |
30 |
|
23AID112 Data Structures & Algorithms L-T-P-C: 2- 0- 2- 3 |
Text Books / References
“Material Informatics: Methods, Tools and Applications” by Olexandr Isayev, Alexander Tropsha and Stefano.
“Informatics for Materials Science and Engineering” by Krishna Rajan
“Machine Learning in Materials Informatics: Methods and Applications by Yuling An
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