If you are interested in attending a workshop, please select them when you register for the conference.
There is no additional fee for workshops.

Finding Your Pathway: Careers and Networking

Luke Roberson
(ACS Careers; NASA Kennedy Space Center)

Details

Introduction to Machine Learning Approach for Materials Discovery

Michael Shatruk (FSU) and Kevin Ryan (FSU)

Details

Finding Your Pathway

Identifying a Career That Matches Your Strengths and Values

Networking: How to Get Started

  • Organizer

    Luke Roberson (ACS Careers; NASA Kennedy Space Center)

  • Audience

    Undergraduate students, Graduate students, Postdoctoral scholars

  • Where and When

    Thursday May 3rd, 1-5 pm

  • Details

    Finding Yourself: Identifying a Career that Matches Your Strengths and Values
    This workshop allows you to self-assess your career values and strengths. Participants will also learn how the four sectors of chemistry employment compare and contrast. This course will also help you determine which sector best aligns to your values and strengths and plan your next steps to obtaining an ideal position.

    Networking: How to Get Started
    This workshop will help participants utilize networking to enhance their job search. Participants will also learn which types of questions to create a natural flow in a networking conversation. The course will also help you create a networking plan to locate and obtain your ideal job.

Introduction to Machine Learning Approach for Materials Discovery

  • Organizers

    Michael Shatruk (FSU) and Kevin Ryan (FSU)

  • Audience

    Graduate students, postdocs, and faculty

  • Where and When

    Thursday May 3rd, 1-5PM

  • Details

    Historically, the approach to the synthesis of new crystalline materials involved systematic construction of phase diagrams or serendipitous discovery. The chemical whitespace, however, is too vast to be explored in such an incremental manner. A new paradigm promoted by the Materials Genome Initiative (MGI) seeks “to discover, develop, and deploy new materials twice as fast.” The idea is to analyze “big data” to accelerate the development of new materials. By processing the wealth of freely accessible scientific data available in the literature, scientists can elucidate complex structure-property relationships to intelligently guide the materials design. The key role in such “big-data” analysis is played by machine learning. Successful application of machine learning approaches in materials chemistry includes discovery of unexpected new class of thermoelectric materials, screening for materials with superior mechanical properties, and prediction of crystal structures for compounds with simple stoichiometries.

    This workshop will provide examples of data mining and machine learning methods with a specific focus on chemical data (crystallographic and materials property databases in particular). The introduction to the practical application will include data processing and preparation aspects, as well as creating and running machine-learning models. Participant will have an opportunity to learn step-by-step how to handle the data, use the models, and interpret the results. No prior programming experience is required.