Syllabus

Introduction to Materials and Manufacturing Informatics: Overview of Materials Informatics Role of AI and Data Science in Materials and Manufacturing Big Data in Materials Science Case Studies in Materials Informatics, Data Acquisition and Management: Data Sources in Materials Science (Experiments, Simulations, Literature), Machine Learning for Materials Science: Supervised and Unsupervised Learning Feature Engineering for Material Properties Model Training and Evaluation (Regression, Classification) Case Studies: Predicting Material Properties High-Throughput Computational Materials: Density Functional Theory (DFT) and Molecular Dynamics (MD) Automated Workflows in Materials Discovery Computational Screening of New Materials, AI-Assisted Materials Design, Additive Manufacturing and Process Informatics, Multi-Scale Modeling for Materials Design, Convolutional Neural Networks (CNNs) for Microstructure Analysis Generative Adversarial Networks (GANs) for Materials Synthesis, AI in Corrosion Prediction Informatics in Polymer and Composite Design Battery Materials Discovery Using AI, Emerging Trends in Smart Manufacturing

1. To Understand the role of data-driven methods in materials science and manufacturing.

2. To explore the impact of artificial intelligence (AI) and machine learning (ML) in material design.

1. Data-Driven Materials Science – Understand how informatics and machine learning techniques are applied to materials discovery, design, and optimization.
2. Materials Data Management – Learning to collect, process, analyze, and interpret materials-related data from experiments, simulations, and databases.
3. Computational Materials Modeling – Gaining knowledge in computational tools and techniques such as density functional theory (DFT), molecular dynamics (MD), and phase-field modeling for materials analysis.
4. Manufacturing Process Optimization – Applying informatics techniques to improve efficiency, reduce defects, and enhance predictive maintenance in manufacturing systems.
5. AI and Machine Learning in Materials & Manufacturing – Understanding how artificial intelligence (AI) and machine learning (ML) models can be trained to predict material properties, optimize compositions, and enhance manufacturing processes.
6. Industry Applications and Case Studies – Exploring real-world applications of informatics in materials science and manufacturing, including additive manufacturing, smart materials, and digital twins

Olexandr Isayev, Alexander Tropsha, Stefano Curtarolo, Materials Informatics: Methods, Tools, and Applications 1st Edition, Wiley-VCH 1st edition (December 4, 2019), , ISBN-10 ? : ? 3527341218

Krishna Rajan, Informatics for Materials Science and Engineering: Data-driven Discovery for Accelerated Experimentation and Application, Butterworth-Heinemann 1st edition (August 7, 2013) , ISBN-10 ? : ? 012394399X

Mark A. Atwater, Materials and Manufacturing: An Introduction to How They Work and Why It Matters, McGraw-Hill Education , ISBN: 9781260122312

Krishna Rajan, Informatics for Materials Science and Engineering: Data-driven Discovery for Accelerated Experimentation and Application, Butterworth-Heinemann 1st edition (August 7, 2013) , ISBN-10 ? : ? 012394399X