Analysis of Ionicity-Magnetism Competition in 2D-MX3 Halides towards a Low-Dimensional Materials Study Based on GPU-Enabled Computational Systems.

Alexey Kartsev,Andrey Chibisov,Sergey Malkovsky

doi:10.3390/nano11112967

Abstract

The acceleration of parallel high-throughput first-principle calculations in the context of 3D (three dimensional) periodic boundary conditions for low-dimensional systems, and particularly 2D materials, is an important issue for new material design. Where the scalability rapidly deflated due to the use of large void unit cells along with a significant number of atoms, which should mimic layered structures in the vacuum space. In this report, we explored the scalability and performance of the Quantum ESPRESSO package in the hybrid central processing unit - graphics processing unit (CPU-GPU) environment. The study carried out in the comparison to CPU-based systems for simulations of 2D magnets where significant improvement of computational speed was achieved based on the IBM ESSL SMP CUDA library. As an example of physics-related results, we have computed and discussed the ionicity-covalency and related ferro- (FM) and antiferro-magnetic (AFM) exchange competitions computed for some CrX compounds. Further, it has been demonstrated how this exchange interplay leads to high-order effects for the magnetism of the 1L-RuCl compound.

Highlights

The ground states calculations were performed using a computational implementation of the density-functional theory (DFT) package Quantum ESPRESSO [11]
Results of ELF calculations for the CrX3 materials shown on the Figure 4 obviously indicate the presence of ionic character, where electrons localized around halides forming
The results observed indicate the efficiency of hybrid computer clusters for collinear and noncollinear first-principle calculations where we employed the IBM ESSL

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In the last decade GPUs have been actively developing the direction of high-performance computing systems, where classical CPU-based systems reached their limits. They have shown high computational efficiency and, have been increasingly used for a simulation purpose in the various fields of physics [6,7,8], where an optimal choice for configuration of the parallel environment is the key point to reach the best performance [9]. We focus on the testing and comparison of computer design methods applied to these materials in the frame of the Quantum ESPRESSO package using both CPU and Nanomaterials 2021, 11, 2967.

Hardware and Parallel Environment

Computational Details

Computational Performance

Accelerating with GPU Computing Example—FM-AFM Exchanges Competition in

Conclusions