Energy & Materials Intern- XRD Advanced Analysis

At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences. This is a paid 12-week internship opportunity and is a hybrid, in-office role. Here’s a glimpse into the Internship experience from some of our TRI interns!

The Team:

The Accelerated Materials Design and Discovery (AMDD) Team at Toyota Research Institute is focused on accelerating the design and discovery of new materials to enable emissions-free mobility. Materials are at the heart of clean energy technologies, such as batteries, fuel cells, solar cells, hydrogen electrolysis, and carbon capture, and discovering new materials can deliver the breakthroughs needed to displace traditional fossil fuels. Our team develops tools to overcome roadblocks in the materials discovery process using software, machine learning, robotics, and automation. Our customers are scientists and engineers advancing new batteries, fuel cell catalysts, and other materials development projects. By increasing the speed of discovery, we can iterate faster and bring new materials from the lab to the world at a much-needed, faster pace.

The Opportunity:

The intern will work within the AMDD team on a project focused on developing advanced analysis tools and pipelines for X-Ray diffraction (XRD) patterns of mixed-metal oxide thin-films. They will use and develop automated Rietveld refinement tools to label XRD patterns and understand structure-property-processing relationships across a compositionally diverse dataset. They will perform phase identification on XRD patterns ranging from unary to ternary metal-oxides, and develop pipelines to perform automatic or human-assisted labeling of experimental data. They will investigate crystallite size, texturing, and strain across the dataset. This work will contribute to the development of automated workflows for phase identification from XRD, an essential bottleneck in accelerating high-throughput materials discovery.