Dynamical properties of hydrogen fluid at high pressures.

The properties of the hydrogen fluid at high pressures are still of interest to the scientific community. The experimentally unreachable dynamical properties could provide new insights into this field. In 2020 [Cheng et al.

The Kob-Andersen model crystal structure: Genetic algorithms vs spontaneous crystallization.

The crystal structure of the Kob-Andersen mixture has been probed by genetic algorithm calculations. The stable structures of the system with different molar fractions of the components have been identified, and their stability at finite temperatures has been verified. It has been found that the structures of composition ABn, where n = 2, 3, or 4, can be formed in the system.

Transfer learning for accurate description of atomic transport in Al-Cu melts.

Machine learning interatomic potentials (MLIPs) provide an optimal balance between accuracy and computational efficiency and allow studying problems that are hardly solvable by traditional methods. For metallic alloys, MLIPs are typically developed based on density functional theory with generalized gradient approximation (GGA) for the exchange-correlation functional. However, recent studies have shown that this standard protocol can be inaccurate for calculating the transport properties or phase diagrams of some metallic alloys.

Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab initio simulations.

Boron phosphide (BP) is a (super)hard semiconductor constituted of light elements, which is promising for high demand applications at extreme conditions. The behavior of BP at high temperatures and pressures is of special interest but is also poorly understood because both experimental and conventional ab initio methods are restricted to studying refractory covalent materials. The use of machine learning interatomic potentials is a revolutionary trend that gives a unique opportunity for high-temperature study of materials with ab initio accuracy.

Time-dependent exchange creates the time-frustrated state of matter.

Magnetic systems governed by exchange interactions between magnetic moments harbor frustration that leads to ground state degeneracy and results in the new topological state often referred to as a frustrated state of matter (FSM). The frustration in the commonly discussed magnetic systems has a spatial origin. Here we demonstrate that an array of nanomagnets coupled by the real retarded exchange interactions develops a new state of matter, time frustrated matter (TFM).