Demystifying Materials Data Science

  • Overview
  • Course Content
  • Requirements & Materials
Overview

Demystifying Materials Data Science

Course Description

This course will introduce you to the basic foundational concepts and tools involved in the application of data science and informatics to problems in multiscale materials discovery, development, and deployment. Beginning with a general introduction for nonexperts, the course will include various hands-on tutorials and demonstrations of emergent open-source tools and approaches customized specifically for materials informatics. Specific focus will be on data accumulation, curation, and management; how to derive surrogate models for materials property predictions, design, and discovery across chemical spaces; multiscale modeling under uncertainty; and the extraction of high-fidelity, reduced-order, Process-Structure-Property (PSP) linkages that constitute the essential knowledge needed to support materials innovation efforts.

Course Content

DATA MANAGEMENT, ANALYSIS, AND CYBERINFRASTRUCTURE

  • Introduction to data management and cyberinfrastructure
  • Introduction to data analysis

HIGH-THROUGHPUT STRATEGIES, MULTISCALE MODELING, AND MACHINE LEARNING

  • High-throughput simulations for materials data generation, analysis, and discovery
  • High-throughput, data rich characterization
  • Uncertainty in multiscale modeling for materials design and development
  • Machine learning for materials discovery

QUANTIFICATION OF THE HIERARCHICAL MATERIAL INTERNAL STRUCTURE

  • Quantification of the hierarchical material internal structure
  • Segmentation of microstructures for digital workflows

REDUCED-ORDER PROCESS-STRUCTURE-PROPERTY (PSP) LINKAGES

  • High-throughput experimental assays for process-structure-property (PSP) linkages in structural materials
  • Framework for reduced-order PSP linkages
  • Case studies in PSP linkages using machine learning and linear regression
  • Regression models for property prediction
Requirements & Materials
Prerequisites

Recommended

Required

  • Undergraduate background in materials science/engineering or related fields
Materials

Provided

  • Lecture notes in a PDF document

Session Details

Who Should Attend

This course is designed for current practitioners in national laboratories, industry, and academia engaged in various aspects of discovery, development, and deployment of new/improved materials in emerging technologies. This course will also be particularly useful for those interested in learning how to combine and leverage experiments, model, and data accelerated materials innovation.

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What You Will Learn

  • Materials data management and analytics
  • Machine learning approaches
  • Approaches for high-throughput rapid screening of new materials
  • A new framework for process-structure-property linkages
  • Accelerated materials discovery, development, and deployment
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How You Will Benefit

  • Understand the emerging concepts in the field of materials data sciences and informatics.
  • Learn how data science can accelerate materials innovation.
  • Review open source materials data and code repositories.
  • Discover how to effectively combine experiments, models, and data.
  • Learn how to apply machine learning to materials innovation.
  • Taught by Experts in the Field icon
    Taught by Experts in the Field
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    Grow Your Professional Network

The course schedule was well-structured with a mix of lectures, class discussions, and hands-on exercises led by knowledgeable and engaging instructors.

- Abe Kani
President

TRAIN AT YOUR LOCATION

We enable employers to provide specialized, on-location training on their own timetables. Our world-renowned experts can create unique content that meets your employees' specific needs. We also have the ability to deliver courses via web conferencing or on-demand online videos. For 15 or more students, it is more cost-effective for us to come to you.

  • Save Money
  • Flexible Schedule
  • Group Training
  • Customize Content
  • On-Site Training
  • Earn a Certificate
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