Maximum Likelihood Analysis of Reaction Coordinates during Solidification in Ni

Abstract

Understanding the underlying mechanism of crystal nucleation during solidification is a fundamental aspect in the prediction and control of materials properties. Classical nucleation theory (CNT) assumes that homogeneous nucleation occurs via random fluctuations within the supercooled liquid, that the structure of the growing clusters resembles the most stable bulk phase, and that the nucleus size is the sole reaction coordinate (RC) of the process. Many materials are, however, known to exhibit multiple steps during crystallization, forming different polymorphs. As a consequence, more complex RCs are often required to capture all relevant information about the process. In this work, we employ transition path sampling together with a maximum likelihood analysis of candidate order parameters to identify suitable reaction coordinates for the nucleation mechanism during solidification in Ni. In contrast to CNT, the analysis of the reweighted path ensemble shows that a pre-structured liquid region that surrounds the crystal cluster is a relevant order parameter that enhances the RC and therefore plays a key role in the description of the growing nucleus and the interfacial free energy. We demonstrate that pre-structured liquid clusters that emerge within the liquid act as precursors of the crystallization in a non-classical two-step mechanism which predetermines the coordination of the polymorphs that are being selected.

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• Role of pre-ordered liquid in the selection mechanism of crystal polymorphs during nucleation
• Template induced precursor formation in heterogeneous nucleation -- Controlling polymorph selection and nucleation efficiency
• Controlling crystallization: What liquid structure and dynamics reveal about crystal nucleation mechanisms
• Interplay of structural and dynamical heterogeneity in the nucleation mechanism in Ni
• Unveiling interatomic distances influencing the reaction coordinates in alanine dipeptide isomerization: An explainable deep learning approach
• Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates