Accelerating material design | katerynaartyushkova

Accelerating material design through structure-to-property modeling

Building structure-to-property relationships is one of the most often attempted research tasks in today’s material chemistry.

We are building structure-to-property relationship models based on statistical correlations between image parameters extracted from microscopic images, chemical composition extracted from spectroscopic data and property of interest.

Chemistry-to-performance correlation

In this study, we have analyzed more than 45 M–N–C electrocatalysts synthesized from three different families of precursors, such as polymer-based, macrocycles, and small organic molecules. Catalysts were electrochemically tested and analyzed structurally using exactly the same protocol for deriving structure-to-property relationships

Digital Image Processing

The goal of Digital Image Processing (DIP) is to find a representation of the intensity distribution of a given image and to convert 2-D images into 1-D image descriptors (values) that can be utilized for quantitative morphology representation and description.

3D electrode morphology via FIB-SEM

3D data visualization of morphology in relationship to transport phenomena

Multiscale analysis

Morphological metrics extracted from the hierarchy of scales between atomic to macroscopic dimensions will be directly related to electrode’ resistance properties. Understanding the relationship between the experimental design of electrode components and subsequent morphology that is responsible for either corrosive or resistant behavior will be a critical input for optimization of durable electrode structures.

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