Signatures of emerging order in complex structures via local structural analysis
ORAL
Abstract
The emergence of crystalline order in assemblies with complex structures, such as Frank–Kasper phases and clathrates, is poorly described by existing theories of particle-by-particle attachment of a fluid to a crystal bulk. Molecular dynamics simulations of self-assembling, idealized particles performed using multi-well, isotropic pair potentials, allow us to explore this question more comprehensively by simulating the growth of structures with varying complexities and coordination numbers [1]. We study this fluid-to-crystal phase transition using two complementary techniques: coordination number analysis coupled with a machine-learning based order parameter [2] that uses spherical harmonics to describe and classify particles by their local environment, and can distinguish crystalline sites of the same coordination number by their differing local geometries. By tracking the how identical particles transition from their less well-defined liquid environment to their distinct “role” (Wyckoff position) within the bulk crystal, we study how the evolution of a particle’s local structure gives way to global crystalline order, which in turn enables us to extract general growth principles across structure types. These insights can guide in the design and assembly of materials with desired structures and functionalities in soft matter systems.
[1] J. Dshemuchadse, P. F. Damasceno, C. L. Phillips, M. Engel, S. C. Glotzer., Proc. Natl. Acad. Sci. USA 118 (21), e2024034118 (2021).
[2] M. Spellings, S. C. Glotzer, AIChE J. 64 (6), 2198–2206 (2018).
[1] J. Dshemuchadse, P. F. Damasceno, C. L. Phillips, M. Engel, S. C. Glotzer., Proc. Natl. Acad. Sci. USA 118 (21), e2024034118 (2021).
[2] M. Spellings, S. C. Glotzer, AIChE J. 64 (6), 2198–2206 (2018).
*This material is based upon work supported by the National Science Foundation under Grant No. DMR-2144094 and was supported by the Cornell Center for Materials Research with funding from the NSF MRSEC program (DMR-1719875). M. M. acknowledges support from the National Science Foundation Graduate Research Fellowship Grant No. DGE-1650441 (2019-2021) and DGE-2139899 (2021-2022) and from the Dolores Zohrab Liebmann Fund Fellowship. M. S. acknowledges resources provided by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute.
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Presenters
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Maya Martirossyan
- Cornell University