This course is an introduction to high-throughput experimental methods that accelerate the discovery and development of new materials.
It is well recognized that the discovery of new materials is the key to solving many technological problems faced by industry and society. These problems include energy production and utilization, carbon capture, tissue engineering, and sustainable materials production, among many others. This course will introduce the learner to a remarkable new approach to materials discovery and characterization: high-throughput materials development (HTMD).
Engineers and scientists working in industry, academic or government will benefit from this course by developing an understanding of how to apply one element of HTMD, high-throughput experimental methods, to real-world materials discovery and characterization problems. Internationally leading faculty experts will provide a historical perspective on HTMD, describe preparation of ‘library’ samples that cover hundreds or thousands of compositions, explain techniques for characterizing the library to determine the structure and various properties including optical, electronic, mechanical, chemical, thermal, and others. Case studies in energy, transportation, and biotechnology are provided to illustrate methodologies for metals, ceramics, polymers and composites.
The Georgia Tech Institute for Materials (IMat) developed this course in order to introduce a broad audience to the essential elements of the Materials Genome Initiative. Other courses will be offered by Georgia Tech through Coursera to concentrate on integrating (i) high-throughput experimentation with (ii) modeling and simulation and (iii) materials data sciences and informatics.
After completing this course, learners will be able to
• Identify key events in the development of High-Throughput Materials Development (HTMD)
• Communicate the benefits of HTMDwithin your organization.
• Explain what is meant by high throughput methods (both computational and experimental), and their merits for materials discovery/development.
• Summarize the principles and methods of high throughput creation/processing of material libraries (samples that contain 100s to 1000s of smaller samples).
• State the principles and methods for high-throughput characterization of structure.
• State the principles and methods for high throughput property measurements.
• Identify when high-throughput screening (HTS) will be valuable to a materials discovery effort.
• Select an appropriate HTS method for a property measurement of interest.
• Identify companies and organizations working in this field and use this knowledge to select appropriate partners for design and implementation of HTS efforts.
• Apply principles of experimental design, library synthesis and screening to solve a materials design challenge.
• Conceive complete high-throughput strategies to obtain processing-structure-property (PSP) relationships for materials design and discovery.
What you will learn
What you should know before you start the course
Frame the grand problem of materials design and how the Materials Genome Initiative approach, which encompasses high-throughput computational and experimental techniques as essential elements, will accelerate materials discovery and development. Provide a historical perspective and future outlook.
This module covers methods to experimentally generate discrete or gradient material libraries for interrogating the influence of composition or microstructure on properties; various process and synthesis methods for different classes of materials are considered
High-Throughput Characterization of Composition and Structure
This module covers techniques suitable for measuring the elemental composition and the structure in the material libraries; techniques for different classes of materials are considered.
High-Throughput Property Measurements
This module covers techniques to experimentally conduct property measurements suitable for high-throughput screening; optical, electronic, mechanical, chemical, and thermal properties are considered.