On the chain-melted phase of matter

Abstract

Various single elements form incommensurate crystal structures under pressure, where a zeolite-type “host” sublattice surrounds a “guest” sublattice comprising 1D chains of atoms. On “chain melting,” diffraction peaks from the guest sublattice vanish, while those from the host remain. Diffusion of the guest atoms is expected to be confined to the channels in the host sublattice, which suggests 1D melting. Here, we present atomistic simulations of potassium to investigate this phenomenon and demonstrate that the chain-melted phase has no long-ranged order either along or between the chains. This 3D disorder provides the extensive entropy necessary to make the chain melt a true thermodynamic phase of matter, yet with the unique property that diffusion remains confined to 1D only. Calculations necessitated the development of an interatomic forcefield using machine learning, which we show fully reproduces potassium’s phase diagram, including the chain-melted state and 14 known phase transitions.

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

“…ML results were obtained from an ensemble of 16 models from which uncertainties have been estimated (shaded area). The results for form II in the bottom panels demonstrate the high transferability of the ML model which can accurately represent the overall lineshape of the unseen molecular crystal temperature [192], which exhibits a complex chain-melted phase that had previously not been characterized in detail. The MD simulations by Robinson et al were initialized in the expected ground-state phase for a given pressure at 200 K, and MD simulations in the NVT statistical ensemble were used to simulate phase transitions with temperature.…”

“…12 ML-based simulations for the exploration of phase diagrams of inorganic materials. a Temperature and pressure dependent phase diagram of potassium obtained from MD simulations using an ML potential [ 192 ]. Each point in the figure represents the result from an individual ML-based MD simulation in the NVT statistical ensemble.…”

Coupled Sublattice Melting and Charge-Order Transition in Two Dimensions

“…Here, we report on a novel 2D transition in a charge-ordered K-Sn alloy monolayer on Si(111), whereby the 'melting' of the charge order in the Sn host lattice is preceded and driven by a true 2D melting transition in the alkali sublattice. Sublattice melting is a well-known phenomenon in bulk ionic conductors where an ionic insulator acquires a highly conductive state when the sublattice containing the lighter ions, typically an alkali or Ag ion, melts while the heavier sublattice or 'host' lattice remains intact (13,14). A similar part-liquid part-crystalline state has been observed in thermoelectric compounds with strong chemical-bond hierarchy (15).…”

Machine learning provides realistic model of complex phase transition

“…Nature has provided us with many more types of phases than the elementary ones-solid, liquid, and gas-that we learn in school textbooks. Some of them can be rather exotic, such as the one considered in the PNAS article by Robinson et al (1), which deals with a remarkable but not so uncommon state of matter found in several elemental solids when they are compressed to gigapascal pressures (1 GPa = 10,000 bar). The solid in this specific case, elemental rubidium, crystallizes in a tetragonal structure composed of two interpenetrating lattices with incommensurate periodicity (Fig.…”

“…Similarly, the intrachain loss of correlation indicates that the simulations may also offer a glimpse into the fundamental but elusive phenomenon of long-range correlations in 1D systems of particles. Both questions will benefit enormously from the availability of a computationally cheap yet realistic model of interaction between Rb atoms in phase III, such as the one developed by Robinson et al (1).…”