Magnesium oxide-water compounds at megabar pressure and implications on planetary interiors

Spectroscopic Study of [Mg, H, N, C, O] Species: Implications for the Astrochemical Magnesium Chemistry

Magnesium is an abundant metal element in space, and magnesium chemistry has vital importance in the evolution of interstellar medium (ISM) and circumstellar regions, such as the asymptotic giant branch star IRC+10216 where a variety of Mg compounds bearing H, C, N, and O have been detected and proposed as the important components in the gas-phase molecular clouds and solid-state dust grains. Herein, we report the formation and infrared spectroscopic characterization of the Mg-bearing molecules HMg, [Mg, N, C], [Mg, H, N, C], [Mg, N, C, O], and [Mg, H, N, C, O] from the reactions of Mg/Mg+ and the prebiotic isocyanic acid (HNCO) in the solid neon matrix. Based on their thermal diffusion and photochemical behavior, a complex reactivity landscape involving association, decomposition, and isomerization reactions of these Mg-bearing molecules is developed, which can not only help understand the chemical processes of the magnesium (iso)cyanides in astrochemistry but also provide implications on the presence of magnesium (iso)cyanates in the ISM and the chemical model for the dust grain surface reactions. It also provides a new paradigm of the key intermediate nature of the cationic complexes in the formation of neutral interstellar species.

Double superionicity in icy compounds at planetary interior conditions

The elements hydrogen, carbon, nitrogen and oxygen are assumed to comprise the bulk of the interiors of the ice giant planets Uranus, Neptune, and sub-Neptune exoplanets. The details of their interior structures have remained largely unknown because it is not understood how the compounds H2O, NH3 and CH4 behave and react once they have been accreted and exposed to high pressures and temperatures. Here we study thirteen H-C-N-O compounds with ab initio computer simulations and demonstrate that they assume a superionic state at elevated temperatures, in which the hydrogen ions diffuse through a stable sublattice that is provided by the larger nuclei. At yet higher temperatures, four of the thirteen compounds undergo a second transition to a novel doubly superionic state, in which the smallest of the heavy nuclei diffuse simultaneously with hydrogen ions through the remaining sublattice. Since this transition and the melting transition at yet higher temperatures are both of first order, this may introduce additional layers in the mantle of ice giant planets and alter their convective patterns.

Vacancy defects impede the transition from peapods to diamond: a neuroevolution machine learning study

Exploration of novel carbon allotropes has been a central subject in materials science, in which carbon peapods hold great potential as a precursor for the development of new carbon allotropes. To enable precise large-scale molecular dynamics simulations, we develop a high-accurate and low-cost machine-learned potential (MLP) for carbon materials using the neuroevolution potential framework. Based on the MLP, we conduct an investigation into the structural transitions of peapod arrays under high-temperature and high-pressure conditions and disclose the impact of vacancy defects. Defects promote the transition from the ordered crystalline structure to the disordered amorphous structure in peapods at low temperatures, while impeding the transition to the ordered diamond structure. Benefiting from the accurate MLP, we are able to reproduce the experimentally observed carbon structures in numerical simulations. We build a diagram summarizing all the structures that appear in the compression simulation of peapod arrays at various temperatures. The present work not only discloses the underlying mechanism of structural transitions from carbon peapods into various functional carbon materials, but also provides a high-accurate and low-cost interatomic potential that shall be valuable in the exploration of novel carbon allotropes.

MAGUS: machine learning and graph theory assisted universal structure searcher

Crystal structure predictions based on first-principles calculations have gained great success in materials science and solid state physics. However, the remaining challenges still limit their applications in systems with a large number of atoms, especially the complexity of conformational space and the cost of local optimizations for big systems. Here, we introduce a crystal structure prediction method, MAGUS, based on the evolutionary algorithm, which addresses the above challenges with machine learning and graph theory. Techniques used in the program are summarized in detail and benchmark tests are provided. With intensive tests, we demonstrate that on-the-fly machine-learning potentials can be used to significantly reduce the number of expensive first-principles calculations, and the crystal decomposition based on graph theory can efficiently decrease the required configurations in order to find the target structures. We also summarized the representative applications of this method on several research topics, including unexpected compounds in the interior of planets and their exotic states at high pressure and high temperature (superionic, plastic, partially diffusive state, etc.); new functional materials (superhard, high-energy-density, superconducting, photoelectric materials), etc. These successful applications demonstrated that MAGUS code can help to accelerate the discovery of interesting materials and phenomena, as well as the significant value of crystal structure predictions in general.