CAREER: Decoding Crystal Growth and Phase Transformations of Complex Structures With Minimalist Self-Assembly Models

NONTECHNICAL SUMMARY

This CAREER award supports theoretical and computational research integrated with education to advance understanding of the process of how crystals grow. Crystalline materials account for a large variety of functional constituents in modern and historic technology. The structure of crystals refers to how the atoms or other microscopic building blocks arrange themselves in a periodic and symmetric way, like the rows of desks in a classroom or tiles on a bathroom floor.

Materials with different crystal structures have different properties and the study of their structure–property relationships lies at the core of materials science and engineering. While crystal structure analysis is an extremely well-developed technique, the process of crystal growth itself is much more difficult to study. Often, the simplest examples of crystal structures are investigated in order to illuminate the ongoing processes in more depth.

However, understanding the formation of different kinds of crystalline order will enable the prediction of the diverse structures that will form in different systems at various compositions, pressures, and other parameters external and internal to the crystal, and therefore will allow for the targeted design of new functional materials. This project aims to elucidate the self-assembly processes that govern the growth of both simple and complex crystal structures, as well as phase transformations between them.

Outreach and education are integral to this project and are intended to empower populations historically underrepresented in the sciences. The research team will invite teachers from rural areas in Central New York to campus for an annual, student-organized summer workshop, and will teach math and science at New York state prisons through the Cornell Prison Education Program.

The principal investigator will assign Wikipedia editing in engineering classes and host Wikipedia edit-a-thons for the scientific community, both improving the students’ scientific writing and communication abilities, as well as Wikipedia’s technical content and the representation of Black, Latine, women, LGBTQ+, and other underrepresented scientists.

TECHNICAL SUMMARY

This CAREER award supports computational research and education on the formation and transformation of diverse crystal structures from simple building blocks. A plethora of structures can be self-assembled from simple, short-range interactions, spontaneously forming long-range ordered crystalline structures with varying complexities, symmetries, and chemical equivalents.

These crystal structures correspond to types of order observed both on the atomic level, as well as on the nano- and mesoscale, in soft condensed matter composed of nanoparticles, polymer micelles, or colloids.

The research goal of this project is to elucidate the self-assembly processes that govern the growth of both simple and complex crystal structures, and phase transformations between them. Specifically, the research team will study the growth of different crystal structures, how different particle attachment patterns depend on the symmetry and complexity of the structure type, and how they vary with the chemistry of the system and constituents.

Through bottom-up knowledge of how ordered structures arise from building-block properties, this project will advance the field of materials design by establishing encyclopedic knowledge about the self-assembly of different structures and types of order in materials, and by implicitly taking into account both thermodynamic and kinetic effects. By using abstract, “agnostic” models for particle interactions, the generated findings will become generalizable across materials systems and length scales, so that this comparative study will yield insights into the fundamentals of crystal growth and solid–solid phase transitions.

Machine learning methods will be applied to gain insights from simulation data created from particle-interaction models.

The education goal of this project is to empower populations historically underrepresented in the sciences. The research team will host student-organized summer workshops for teachers from rural areas in Central New York. Additionally, members of the research team will teach math and science at New York state prisons through the Cornell Prison Education Program.

The principal investigator will assign Wikipedia editing in engineering classes and improve the students’ scientific writing and communication abilities and Wikipedia’s technical content. The principal investigator will also host Wikipedia edit-a-thons within the scientific community to improve the representation of Black, Latine, women, LGBTQ+, and other underrepresented scientists on Wikipedia, and to empower students and academics to contribute to Wikipedia for both science communication and advocacy.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.