Machine learning based insights of seeded congruent crystal growth of LiNbO 3 in glass

R Thapa and ME Mckenzie and E Musterman and J Kaman and V Dierolf and H Jain, ACTA MATERIALIA, 276, 120115 (2024).

DOI: 10.1016/j.actamat.2024.120115

The seeded crystal growth of LiNbO 3 in glass under the isothermal conditions has been studied using a machinelearned clustering algorithm trained on a combination of static and dynamic structural features. Our findings contradict the sharp crystal-glass interface assumption of classical nucleation theory (CNT). The growth of the seed occurs via the attachment of a group of atoms rather than single atoms. The predictions from the machine-learned simulations helped us compare the growth rate of seeds across various initial seed-sizes and temperature. Simulations with multiple seeds show that the growth rate of a seed is enhanced by the presence of another seed in its vicinity.

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