Maximum entropy mediated liquid-to-solid nucleation and transition

TL;DR

This paper introduces a maximum entropy-based algorithm that biases molecular dynamics simulations using RDF data, enabling controlled nucleation and phase transitions in liquids to study crystallization and improve atomic models.

Contribution

The novel algorithm combines MD potentials with RDF data under maximum entropy to guide simulations towards desired structures and phases, including crystallization.

Findings

Successfully reproduces RDF and ADF of water models

Induces crystallization of water to ice and TiO2 to rutile or anatase

Potential applications in model improvement and phase transition studies

Abstract

Molecular Dynamics (MD) simulations are a powerful tool for studying matter at the atomic scale. However, to simulate solids, an initial atomic structure is crucial for the successful execution of MD simulations, but can be difficult to prepare due to insufficient atomistic information. At the same time Wide Angle X-ray Scattering (WAXS) measurements can determine the Radial Distribution Function (RDF) of atomic structures. However, the interpretation of RDFs is often challenging. Here we present an algorithm that can bias MD simulations with RDFs by combining the information of the MD atomic interaction potential and the RDF under the principle of maximum relative entropy. We show that this algorithm can be used to adjust the RDF of one liquid model, e.g., the TIP3P water model, to reproduce the RDF and improve the Angular Distribution Function (ADF) of another model, such as the TIP4P/2005 water model. In addition, we demonstrate that the algorithm can initiate crystallization in liquid systems, leading to both stable and metastable crystalline states defined by the RDF, e.g., crystallization of water to ice and liquid TiO2 to rutile or anatase. Finally, we discuss how this method can be useful for improving interaction models, studying crystallization processes, interpreting measured RDFs, or training machine learned potentials.