Structure and polymerization of liquid sulfur across the $\lambda$-transition

Abstract

The anomalous $\lambda$-transition of liquid sulfur, which is supposed to be related to the transformation of eight-membered sulfur rings into long polymeric chains, has attracted considerable attention. However, a detailed description of the underlying dynamical polymerization process is still missing. Here, we study the structures and the mechanism of the polymerization processes of liquid sulfur across the $\lambda$-transition as well as its reverse process of formation of the rings. We do so by performing ab-initio-quality molecular dynamics simulations thanks to a combination of machine learning potentials and state-of-the-art enhanced sampling techniques. With our approach, we obtain structural results that are in good agreement with the experiments and we report precious dynamical insights into the mechanisms involved in the process.

References in corpus (17)

• Canonical sampling through velocity-rescaling
• Advanced capabilities for materials modelling with Quantum ESPRESSO
• Separable Dual Space Gaussian Pseudo-potentials
• Relativistic separable dual-space Gaussian Pseudopotentials from H to Rn
• CP2K: An Electronic Structure and Molecular Dynamics Software Package -- Quickstep: Efficient and Accurate Electronic Structure Calculations
• PLUMED 2: New feathers for an old bird
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
• DeePMD-kit v2: A software package for Deep Potential models
• Rethinking Metadynamics: from bias potentials to probability distributions
• Silicon liquid structure and crystal nucleation from ab-initio deep Metadynamics
• A unified framework for machine learning collective variables for enhanced sampling simulations: $\text{mlcolvar}$
• From enhanced sampling to reaction profiles
• Liquid-Liquid Critical Point in Phosphorus
• Physical Properties of Sulfur Near the Polymerization Transition
• DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation

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• Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
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• Atomic bonding in equilibrium single-component melts. The cases of arsenic, antimony and bismuth