Compositional fluctuations and polymorph selection in crystallization of model soft colloids

A Abhilasha Kumari (School of Chemical and Materials Sciences, Indian Institute of Technology Goa 1 , Ponda, Goa 403401,) G Gadha Ramesh (Department of Physics, Indian Institute of Science Education and Research (IISER) Tirupati 2 , Tirupati, Andhra Pradesh 517619,) D Debasish Koner (Department of Chemistry, Indian Institute of Technology 3 , Hyderabad, Telangana 502285,) R Rakesh S. Singh (Department of Chemistry, Indian Institute of Science Education and Research (IISER) Tirupati 4 , Tirupati, Andhra Pradesh 517619,) M Mantu Santra (School of Chemical and Materials Sciences, Indian Institute of Technology Goa 1 , Ponda, Goa 403401,)

Abstract

Understanding polymorph selection in atomic and molecular systems and its control through thermodynamic conditions and external factors (such as seed characteristics) is fundamental to the design of targeted materials and holds great significance in materials sciences. In this work, using Monte Carlo simulations on the Gaussian core model and hard-core Yukawa colloidal systems, we investigated the control of polymorph selection and explored the underlying mechanisms by tuning thermodynamic parameters. We demonstrate that by carefully modifying the free energy landscape to render the globally stable face-centered cubic (FCC) phase metastable with respect to the body-centered cubic (BCC) phase, the polymorphic identity of particles transitions from FCC-dominated to BCC-dominated via an intermediate regime where both phases nucleate—either selectively or competitively—giving rise to a critical-like composition fluctuation of the growing solid-like cluster during the nucleation process. We further probed the critical solid-like cluster compositions, especially in the vicinity of the fluid–BCC–FCC triple point, where the three phases coexist, and observed an interpenetrating arrangement of FCC- and BCC-like particles rather than a commonly observed non-classical core–shell-like two-step nucleation scenario. In addition, we developed a supervised machine learning approach based on structural descriptors derived from persistent homology, a topological data analysis method, to uncover the polymorph selection signatures encoded in local structural fluctuations of the metastable fluid. We believe that the insights gained from this work have the potential to add to the ongoing efforts to control crystallization pathways to obtain the desired functional material.

Article Details

Volume / Issue Vol. 163, Issue 23
Published December 21, 2025
ISSN 0021-9606
Publisher American Institute of Physics

Journal Info

The Journal of Chemical Physics

American Institute of Physics

ISSN: 0021-9606 Physical Sciences

Authors (5)

A

Abhilasha Kumari

School of Chemical and Materials Sciences, Indian Institute of Technology Goa 1 , Ponda, Goa 403401,

G

Gadha Ramesh

Department of Physics, Indian Institute of Science Education and Research (IISER) Tirupati 2 , Tirupati, Andhra Pradesh 517619,

D

Debasish Koner

Department of Chemistry, Indian Institute of Technology 3 , Hyderabad, Telangana 502285,

R

Rakesh S. Singh

Department of Chemistry, Indian Institute of Science Education and Research (IISER) Tirupati 4 , Tirupati, Andhra Pradesh 517619,

M

Mantu Santra

School of Chemical and Materials Sciences, Indian Institute of Technology Goa 1 , Ponda, Goa 403401,