Predictive Models of Self-Assembling Crystalline Structures

Crystalline materials are composed of uniform building blocks, such as water molecules in ice and silicon dioxide in quartz, that arrange themselves into tightly organized three-dimensional patterns. Crystalline materials are often self-assembling, meaning that once the crystalline structure starts to form, small local forces will pull any loose, nearby building blocks into the ordered pattern. In many cases, the self-same building blocks can arrange themselves into a number of distinct patterns, called phases, with different phases arising at different temperatures and pressures.

Crystalline materials are a core component in many established and emerging technologies, including semiconductors and lasers. But our understanding of how crystals grow is limited, which has consequences for the development of new materials. With this CAREER award, Julia Dshemuchadse, Materials Science and Engineering, is elucidating the processes that govern the self-assembly of crystalline structures and solid-to-solid transformations between phases. Using computational simulations and machine learning, researchers will illuminate various patterns of building-block attachment that arise within a range of simple and complex crystalline structures. Researchers will also investigate how patterns of particle attachment depend on 1) the properties of the building blocks and 2) the temperature, pressure, and other conditions in which the building blocks are crystallizing.

The results of this project could enable researchers to predict the crystalline structures that particular building blocks will form at any given temperature, pressure, and other adjustable parameters. Ultimately, this research could enable more precise control of crystalline growth, allowing for the targeted design of innovative materials with unique properties.

Cornell Researchers

Funding Received

$580 Thousand spanning 5 years