November 2–4, 2021

Online Course

Tuesday, 9 a.m.–12 p.m. EDT, 1 p.m.–4 p.m. EDT
Wednesday, 9 a.m.–12 p.m. EDT, 1 p.m.–4 p.m. EDT
Thursday, 9 a.m.–12 p.m. EDT

Course Curriculum

Curriculum Flyer

Course Schedule

Go to the Instructors section to view bios and learn more about their research and professional experience.

Course Modules

Module 1, The Road to AI: A Historical and Computational Perspective

Instructor: Marius Stan

This module will review the evolution of AI from a historical perspective. It will then provide a high-level overview of some key components of AI that will be covered in much more detail in the subsequent modules. Here, the focus will be specifically on components that are employed in materials science and engineering, and materials-related manufacturing, including machine learning (and deep learning), computer vision, natural language processing, and autonomous research. Finally, some specific case studies of the application of AI in materials research and design will be provided.

Learning Objectives

• Gain a historical review of the evolution of AI
• Attain a high-level overview and understanding of some key AI methodologies used in materials science and engineering, including machine learning, deep learning, computer vision, natural language processing, and autonomous research
• Obtain some examples of the application of AI to materials research problems

Module 2, Introduction to Machine Learning and Deep Learning for Materials Science

Instructors: Ankit Agrawal, Bryce Meredig

The increasing availability of data from the first three paradigms of science (experiments, theory, and simulations), along with advances in artificial intelligence and machine learning (AI/ML) techniques has offered unprecedented opportunities for data-driven science and discovery, which is the fourth paradigm of science. Within the arena of AI/ML, deep learning (DL) has emerged as a game-changing technique in recent years with its ability to effectively work on raw big data, bypassing the (otherwise crucial) manual feature engineering step traditionally required for building accurate ML models. This module will introduce some of the fundamental concepts in AI/ML/DL along with illustrative examples of their application in materials science and engineering. We will discuss the unique aspects of applying AI/ML/DL to materials science specifically, and also how to be an informed "consumer" of these data-driven models.

Learning Objectives

• Become familiar with the basic components of a data-driven AI/ML/DL workflow
• Get a high-level understanding of the workings of commonly used AI/ML/DL algorithms and evaluation methodologies
• Learn how to gainfully apply AI/ML/DL on materials data for property prediction and inverse design with real-world examples
• Learn why materials science is a unique use case for AI/ML/DL, and some domain-specific challenges and considerations associated with applying these tools

Format: Live instruction with slides and Q&A.

Module 3: Computer Vision for Microstructural Analysis

Instructors: Elizabeth A. Holm, Samantha Daly

Computer vision incorporates AI tools such as convolutional neural networks (CNNs) and machine learning to extract information from images. When applied to materials science images, computer vision enables autonomous and high-throughput analysis, including classification, segmentation, and characterization. In this module, we will introduce computer vision for microstructural image analysis through lectures and a hands-on tutorial.

Learning Objectives

• Learn about the capabilities of computer vision for microstructural image analysis
• Understand how the basic building blocks of computer vision work together to accomplish image analysis goals
• Recognize the data attributes that support successful computer vision applications
• Practice unsupervised and supervised machine learning to characterize defects in a set of microstructural images

Format: Synchronous / asynchronous lecture and hands-on, cloud-based tutorial

Module 4, Autonomous Research: Theory and Implementation

Instructors: Benji Maruyama, Kristofer Reyes

This tutorial describes the basic theory underpinning many autonomous closed-loop research platforms. Additionally, we describe the practical process of connecting the developed models and algorithms to machine-controllable equipment. Finally, we close with a discussion of various software tools relevant to autonomous research.

Learning Objectives

• Learn the basic ingredients of closed-loop design
• Understand how to perform simulations to assess modeling and algorithmic choices
• Appreciate the practicalities of developing their own autonomous platforms

Format: Lecture and Jupyter notebook demos

Module 5, AI/ML for Materials Manufacturing: Understanding the Applications, Building Predictive Modeling, and Uncertainty Quantification

Instructors: David Blondheim, Jr., Vipul Gupta, Sayan Ghosh

The first half of this module will review production examples of applications of data collection and machine learning in production manufacturing environments. With a focus on aluminum die casting, topics including data collection misclassifications, dealing with highly unbalanced data sets, data space overlap, and cost/accuracy needs of machine learning (ML) systems will be reviewed. In the second half, we will discuss probabilistic machine learning focusing on building Gaussian Process model and demonstrate its use for predictive modeling, uncertainty quantification, and sensitivity analysis. Lastly, we will discuss active learning or intelligent experimentation, which can reduce experimental cost for model building by ML-enhanced intelligent decision making for experimental planning. Jupyter notebook (with Python) will be used to demonstrate the modeling process vis simple examples. Hand-on activity: attendees will apply Prob-ML code using their formatted dataset and model results will be briefly discussed at the end of the workshop.

Learning Objectives

• Understand applications and challenges of data collection and ML within production manufacturing environments (focus on aluminum die casting)
• Complete cost analysis and understand the critical error threshold for ML implementation in manufacturing
• Build a good predictive model, interpret model output, and quantify uncertainty for additive manufacturing parameter development
• Learn how to use predictive model to intelligent design future experiments

• First Section (Blondheim): Live instruction, in-class homework activity, review
• Second Section (Gupta/Ghosh): Live instruction, hands-on activity, quiz

For More Information

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