Authors:

Kyenret Yakubu Ayuba

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About Paper:

Nucleation, a critical first step in crystallization, remains one of the most challenging phenomena to understand due to its stochastic nature. Nucleation has wide-ranging implications and is crucial for various applications, including materials science and pharmaceutical, nutraceutical, agrochemical, fine chemical, and specialty chemical manufacturing. This project aims to gain fundamental insights into the nucleation mechanism by simulating the amorphization process of molecular clusters. Specifically, molecular dynamics (MD) simulations are being performed using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) software package to compute the amorphization kinetics of initially crystal-like clusters. Each MD simulation provides dynamic data about the state of a system at multiple timesteps, capturing the movement and interactions of atoms as a cluster structure transitions toward a randomly packed state. For each atom in the system, the output file details its type, unique identifier, and scaled (fractional) coordinates at each timestep within the simulation box. The resulting trajectories from the MD simulations are being visualized using Visual Molecular Dynamics (VMD). Principal component analysis (PCA) will then be conducted on the simulation trajectories to reduce their dimensionality and help identify key configurations, including energy minima, that are associated with the amorphization process. Simulating the amorphization process offers insights into how a system departs from its crystalline state, revealing factors that dictate the initial occurrence of nucleation. The ultimate goal is to develop a machine learning model trained on the simulated trajectories to learn symmetry information that is created through nucleation. Keywords: Nucleation; Molecular Dynamics (MD) Simulation; LAMMPS; Organic Molecules; Molecular Clusters

Source:

Purdue University / 2024

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Co-authors:

Kyenret Yakubu Ayuba