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Porhouy Minh

Publications and source records attributed to Porhouy Minh.

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The Role of Concentration in Determining NaCl Nucleation Mechanism: A Story of Pathways Coexistence

Deciphering the microscopic details of crystal nucleation remains a key open problem in chemical physics. For aqueous sodium chloride (NaCl), the extent to which nucleation involves amorphous ion aggregation, and the supersaturation regime in which two-step-like behavior emerges, remains actively debated. In this work, we examine NaCl nucleation at 12, 13, and 14 mol/kg ($S = 3.2, 3.5, 3.8$) using unbiased path sampling with $\infty$RETIS. Through a combination of reaction coordinate analysis, joint and conditional probability distributions, diffusion tensor analysis, and conditional free energy landscapes, we identify nucleation pathways without a priori mechanistic assumptions. While all concentrations are governed by the same reaction coordinate, indicating that nucleus growth and structural ordering are central to crossing the barrier, the nature of reactive trajectories changes with supersaturation. At lower concentrations, nucleation mainly occurs via tightly coupled increases in cluster size and ordering, compatible with a one-step-like mechanism. As concentration increases, amorphous aggregation becomes increasingly decoupled from crystallization, and pathways involving substantial amorphous growth prior to crystallization become progressively more probable. Importantly, these pathways coexist within a single broad reaction channel rather than proceeding through distinct metastable intermediate states. Together, these results reconcile earlier, contradictory reports on NaCl nucleation mechanisms and support a view of nucleation as an ensemble of competing reactive pathways whose relative probabilities vary continuously with supersaturation. More broadly, our findings illustrate that substantial mechanistic diversity can emerge within a classical nucleation framework, without requiring distinct non-classical descriptions.

cond-mat.stat-mech

Path Sampling for Rare Events Boosted by Machine Learning

The study by Jung et al. (Jung H, Covino R, Arjun A, et al., Nat Comput Sci. 3:334-345 (2023)) introduced Artificial Intelligence for Molecular Mechanism Discovery (AIMMD), a novel sampling algorithm that integrates machine learning to enhance the efficiency of transition path sampling (TPS). By enabling on-the-fly estimation of the committor probability and simultaneously deriving a human-interpretable reaction coordinate, AIMMD offers a robust framework for elucidating the mechanistic pathways of complex molecular processes. This commentary provides a discussion and critical analysis of the core AIMMD framework, explores its recent extensions, and offers an assessment of the method's potential impact and limitations.

physics.comp-ph

Crystal Nucleation Kinetics and Mechanism: Influence of Interaction Potential

Modulating liquid-to-solid transitions and the resulting crystalline structure for tailored properties is much desired. Colloidal systems are exemplary to this end, but the fundamental knowledge gaps in relating the influence of intermolecular interactions to crystallization behavior continue to hinder progress. In this study, we address this knowledge gap by studying nucleation and growth in systems with modified Lennard-Jones potential. Specifically, we study the commonly used 12-6 potential and a softer 7-6 potential. The thermodynamic state point for the study is chosen such that both systems are investigated at the same level of supercooling and pressure. Under these conditions, we find that the nucleation rate for both systems is comparable. Interestingly, the nucleation pathways and resulting crystal structures are different. In the 12-6 system, nucleation and growth occur predominantly through the FCC structure. Softening the potential alters the critical nucleus composition and introduces two distinct nucleation pathways. One pathway predominantly leads to the nucleus with a body-centered cubic (BCC) structure, while the other favors the face-centered cubic (FCC) arrangement. Our study illustrates that polymorph selection can be achieved through modifications to intermolecular interactions without impacting nucleation kinetics. The results have significant implications in designing approaches for polymorph selection and modulating self-assembly mechanisms.

cond-mat.stat-mech

LeaPP: Learning Pathways to Polymorphs through machine learning analysis of atomic trajectories

Understanding the mechanisms underlying crystal formation is crucial. For most systems, crystallization typically goes through a nucleation process that involves dynamics that happen at short time and length scales. Due to this, molecular dynamics serves as a powerful tool to study this phenomenon. Existing approaches to study the mechanism often focus analysis on static snapshots of the global configuration, potentially overlooking subtle local fluctuations and history of the atoms involved in the formation of solid nuclei. To address this limitation, we propose a methodology that categorizes nucleation pathways into reactive pathways based on the time evolution of constituent atoms. Our approach effectively captures the diverse structural pathways explored by crystallizing Lennard-Jones-like particles and solidifying Ni$_3$Al, providing a more nuanced understanding of nucleating pathways. Moreover, our methodology enables the prediction of the resulting polymorph from each reactive trajectory. This deep learning-assisted comprehensive analysis offers an alternative view of crystal nucleation mechanisms and pathways.

cond-mat.stat-mech