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利用密度泛函理论(DFT)加人工智能研究了GdAl在压力和温度下的弹性和稳定性。

Elasticity and stability of GdAl under pressure and temperature investigated using DFT+AI.

作者信息

Ebrahimi-Jaberi Reyhaneh, Jalali-Asadabadi Saeid

机构信息

Department of Physics, Faculty of Physics, University of Isfahan (UI), Hezar Jerib Avenue, 8174673441, Isfahan, Iran.

出版信息

Sci Rep. 2025 May 4;15(1):15573. doi: 10.1038/s41598-025-99186-3.

Abstract

The cubic ferromagnetic Laves phase intermetallic compound [Formula: see text] is a promising candidate for aerospace, defense, and advanced engineering applications due to its thermal stability and reliable elastic properties under pressure. However, two key gaps persist: discrepancies between theoretical and experimental elastic constants, and a lack of systematic pressure-dependent investigations. This study addresses these gaps, highlighting [Formula: see text]'s exceptional thermal stability, with melting temperatures rising linearly under pressure, its near-isotropic compressive behavior, and mild anisotropy in shear and Young's moduli. Using density functional theory, elasticity theory, and AI-driven neural networks, we systematically analyzed the elasticity and stability of the system under pressure and temperature. A rigorous energy-based methodology resolves the first gap, setting a benchmark for cubic systems. To address the second gap, we analyzed mechanical stability up to 20 GPa via the Born stability criteria, finding consistent increases in elastic constants, bulk modulus, and Young's modulus under compression. Phonon dispersion and density of states analyses confirm dynamic stability and reveal that low-frequency acoustic modes dominated by Gd atoms drive elastic behavior, reflecting spin-dominated mechanics. Poisson's ratio shows mild anisotropy, while ductility assessments reaffirm the material's brittle nature, consistent with Laves phase intermetallics. By integrating advanced computational methods and AI predictions, this work resolves theoretical-experimental discrepancies, establishes a framework for spin-dominated systems, and positions [Formula: see text] as a benchmark for spin-lattice interactions and anisotropy in next-generation engineering under pressure.

摘要

立方铁磁拉夫斯相金属间化合物[化学式:见原文]因其热稳定性以及在压力下可靠的弹性性能,是航空航天、国防和先进工程应用的一个有潜力的候选材料。然而,两个关键差距仍然存在:理论弹性常数与实验弹性常数之间的差异,以及缺乏系统的压力依赖性研究。本研究弥补了这些差距,突出了[化学式:见原文]的卓越热稳定性,其熔化温度在压力下呈线性上升,其近各向同性的压缩行为,以及剪切模量和杨氏模量的轻微各向异性。利用密度泛函理论、弹性理论和人工智能驱动的神经网络,我们系统地分析了该体系在压力和温度下的弹性和稳定性。一种基于能量的严格方法解决了第一个差距,为立方体系设定了一个基准。为了弥补第二个差距,我们通过玻恩稳定性准则分析了高达20吉帕的力学稳定性,发现在压缩下弹性常数、体积模量和杨氏模量持续增加。声子色散和态密度分析证实了动态稳定性,并揭示了由钆原子主导的低频声学模式驱动弹性行为,反映了自旋主导的力学特性。泊松比显示出轻微的各向异性,而延展性评估再次证实了该材料的脆性本质,这与拉夫斯相金属间化合物一致。通过整合先进的计算方法和人工智能预测,这项工作解决了理论与实验的差异,建立了自旋主导体系的框架,并将[化学式:见原文]定位为下一代压力工程中自旋-晶格相互作用和各向异性的基准。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de45/12050340/8f422dfdae34/41598_2025_99186_Fig1_HTML.jpg

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