In the field of material design, traditional crystal structure prediction approaches require extensive structural sampling through computationally expensive energy minimization methods using either force fields or quantum mechanical simulations. While emerging artificial intelligence (AI) generative models have shown great promise in generating realistic crystal structures more rapidly, most existing models fail to account for the unique symmetries and periodicity of crystalline materials, and they are limited to handling structures with only a few tens of atoms per unit cell. Here, we present a symmetry-informed AI generative approach called Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal) that overcomes these limitations. Our method generates initial structures using AI models trained on an augmented small dataset, and then optimizes them using machine learning structure descriptors rather than traditional energy-based optimization. We demonstrate the effectiveness of LEGO-xtal by expanding from 25 known low-energy sp2 carbon allotropes to over 1,700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and next-generation battery materials.

This paper presents novel approaches to parallelizing particle interactions on a GPU when there are few particles per cell and the interactions are limited by a cutoff distance. The paper surveys classical algorithms and then introduces two alternatives that aim to utilize shared memory. The first approach copies the particles of a sub-box, while the second approach loads particles in a pencil along the X-axis. The different implementations are compared on three GPU models using Cuda and Hip. The results show that the X-pencil approach can provide a significant speedup but only in very specific cases.

Based on first-principles calculations and ab initio molecular dynamics simulations, the polymerisation of the unsaturated cis dinitrogen-difluoride (cis-N2F2) molecular compound is investigated. The thermodynamic, dynamical and thermal stabilities of the nitrogen fluorine NF system are investigated at conditions of 0-3000 K and 0-200 GPa. The cis-N2F2 molecule is a suitable precursor to obtain one-dimensional polymerized nitrogen-fluorine (poly-NF) chains at a pressure above 90 GPa and at a temperature around 1900 K. Importantly, these poly-NF chains can be quenched to room conditions, and potentially serve as a High-energy-density materials (HEDM). It has been established that when Al is utilised as a reducing agent, poly-NF chains exhibit a gravimetric energy density of 13.55 kJ/g, which exceeds that of cubic gauche nitrogen (cg-N, 9.70 kJ/g). This is attributable to the presence of both polymerised nitrogen and strong oxidising F atoms.