Computational Materials Science最新文献

Gravity and composition modulated solidification and mechanical properties of Al-Cu nanostructures 重力和成分调制Al-Cu纳米结构的凝固和力学性能

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-17 DOI: 10.1016/j.commatsci.2026.114504

Apurba Sarker, Sourav Saha

The future of space exploration and human settlement beyond Earth hinges on a deeper understanding of in-space manufacturing processes. The unique physical conditions and scarcity of experimental data demand robust computational models to investigate the atomic-scale physics of solidification. This work presents a molecular dynamics (MD) model to examine the solidification behavior of the

A l

–

C u

binary alloy, focusing on the influence of varying compositions and gravity levels (Earth, Lunar, Martian, and microgravity) on atomistic solidification mechanisms and the resulting mechanical properties — specifically, hardness — of as-solidified nanostructures. Hardness is evaluated via nanoindentation simulations. The study confirms that gravitational forces significantly affect the solidification pathways of

A l

–

C u

alloys. Notably, by tuning alloy composition, the influence of gravity can be modulated—and in some cases, even reversed. Moreover, hardness exhibits a coupled dependence on both composition and gravity, offering a promising avenue for bottom-up design of components tailored for extraterrestrial environments. The article delves into the nanoscale physical mechanisms underlying these phenomena and outlines future directions for extending this modeling framework to broader applications.

太空探索和人类在地球之外定居的未来取决于对太空制造过程的更深入了解。独特的物理条件和实验数据的缺乏需要强大的计算模型来研究原子尺度的凝固物理。这项工作提出了一个分子动力学（MD）模型来研究Al-Cu二元合金的凝固行为，重点研究了不同成分和重力水平（地球、月球、火星和微重力）对原子凝固机制的影响，以及固化纳米结构的机械性能，特别是硬度。通过纳米压痕模拟来评估硬度。研究证实，重力对Al-Cu合金的凝固路径有显著影响。值得注意的是，通过调整合金成分，重力的影响可以被调节——在某些情况下，甚至可以被逆转。此外，硬度表现出对成分和重力的耦合依赖，为为地外环境量身定制的自下而上的组件设计提供了有希望的途径。本文深入研究了这些现象背后的纳米级物理机制，并概述了将该建模框架扩展到更广泛应用的未来方向。

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引用次数: 0

Impingement of nanoscale droplets upon deposited ones at different given conditions: A molecular dynamics study 纳米液滴在不同条件下撞击沉积的纳米液滴：分子动力学研究

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-17 DOI: 10.1016/j.commatsci.2026.114516

Yuanyuan Tian , Jinfeng Du , Zhiyang Li , Xin He , Benxi Zhang

Impingement of deposited droplets with coming ones is regarded as a fundamental process in terms of many practical applications, especially for micro-nano systems. However, the understanding of such systems from dynamic evolution to effect of characteristic parameters remains limited and unsatisfactory. To address this gap, the current study employs molecular dynamics (MD) simulations to unravel the process of impacting deposited droplets with coming on the molecular level. By exploring the effect of various parameter groups, We, Δ, and θ₀, systematically, we map all observed outcomes into a series of phase diagrams that encompass four distinct regimes, including deposition, regular bounce, bounce with closed holes, and breakup bounce. The free evolution of targeted systems, critical We between different regimes, and contact time have been investigated and discussed through directly extracting data from numerical simulations and theoretical calculations. Ultimately, we develop a novel system with wrapped nanoparticles to quickly sweep deposited droplets in order to recover hydrophobicity of solid surfaces. The underlying mechanisms are attributable to enhanced energy conversion between merged droplets and wrapped nanoparticles. The current work helps researchers obtain comprehensively insights into the nanoscale collision of droplets with unequal size. Moreover, this approach opens a window to recover the superhydrophobicity of solid surfaces that are contaminated by deposited water droplets.

在许多实际应用中，特别是在微纳系统中，沉积的液滴与到来的液滴的碰撞被认为是一个基本的过程。然而，对这类系统从动态演化到特征参数影响的理解仍然有限，令人不满意。为了解决这一问题，目前的研究采用分子动力学（MD）模拟来揭示在分子水平上影响沉积液滴的过程。通过系统地探索各种参数组We， Δ和θ0的影响，我们将所有观察到的结果映射到一系列相图中，这些相图包含四种不同的制度，包括沉积，规则弹跳，封闭孔弹跳和破裂弹跳。通过直接从数值模拟和理论计算中提取数据，研究和讨论了目标系统的自由演化、不同状态之间的临界We和接触时间。最终，我们开发了一种新型的包裹纳米颗粒系统，可以快速扫描沉积的液滴，以恢复固体表面的疏水性。潜在的机制是由于合并的液滴和包裹的纳米颗粒之间的能量转换增强。目前的工作有助于研究人员全面了解大小不等的液滴在纳米尺度上的碰撞。此外，这种方法为恢复被沉积的水滴污染的固体表面的超疏水性打开了一扇窗。

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引用次数: 0

A machine learning approach to modeling the effects of fiber shape and interphase on the thermoelastic properties of composites 用机器学习方法模拟纤维形状和界面相对复合材料热弹性性能的影响

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-16 DOI: 10.1016/j.commatsci.2026.114514

Yang Sun , Jia Liu , Guangzhao Deng , Shuan Ma , Dengbao Xiao

This study proposes an innovative micromechanics-based deep neural network method to efficiently investigate the effects of fiber shape and interphase on the thermoelastic properties of unidirectional composites. Firstly, this work establishes a micromechanical finite element approach by simulating the internal microstructure of the composite and verifies its rationality by comparing it with experimental results. Subsequently, using DOE sampling method based on global arrangement, data groups for training are obtained through the finite element simulation, and the machine learning model is further constructed utilizing deep neural network algorithm. The effectiveness of the machine learning model is validated by comparing the true values from the finite element simulation with the predicted values from the machine learning. Finally, a comprehensive investigation is conducted to elucidate the effects of fiber concentration and morphology, interphase concentration and characteristics on the thermoelastic behavior of composites. The results show that the established machine learning model provides a fast and accurate prediction for the thermoelastic properties of composites considering microstructural features.

本文提出了一种基于微力学的深度神经网络方法，以有效地研究纤维形状和界面相对单向复合材料热弹性性能的影响。首先，通过模拟复合材料内部微观组织，建立微观力学有限元方法，并与实验结果进行对比，验证其合理性。随后，采用基于全局排列的DOE采样方法，通过有限元仿真获得训练数据组，并利用深度神经网络算法进一步构建机器学习模型。通过将有限元模拟的真实值与机器学习的预测值进行比较，验证了机器学习模型的有效性。最后，全面研究了纤维浓度和形态、界面浓度和特性对复合材料热弹性性能的影响。结果表明，所建立的机器学习模型能够快速准确地预测复合材料的热弹性性能。

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引用次数: 0

Tuning the hydrogen storage properties of KMgH3 through metal doping, vacancy engineering, and compressive strain: A first principles study 通过金属掺杂、空位工程和压缩应变调整KMgH3的储氢性能：第一性原理研究

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-16 DOI: 10.1016/j.commatsci.2026.114510

Imane Bezzaoui , Soukaina Elhalimi , Abdelmajid El Badraoui , Abdelmajid Elmansouri , Najim Tahiri , Omar El Bounagui

In this study, we investigate the hydrogen storage potential of KMgH₃ using a comprehensive first-principles approach that combines density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. We examine the effect of 25% potassium substitution with aluminum and titanium (Al,Ti), the introduction of potassium vacancies, and the application of compressive strain on the doped KMgH₃ structures, focusing on their structural stability, thermodynamic, hydrogen storage, and electronic properties. Our results show that both doping and vacancy engineering significantly reduce the formation enthalpy and desorption temperature, improving dehydrogenation performance. Notably, potassium vacancies increase the gravimetric capacity from 4.55 wt% to 5.33 wt%, while compressive strain reduces the formation enthalpy to −40.62 kJ/mol.H₂ and the desorption temperature to 313.46 K. The system exhibits structural and thermal stability under these modifications, as confirmed by elastic constant analysis and AIMD simulations at ambient temperature. To explain this reduction, we examined the electronic structure, which reveals that the reduction in potassium atoms introduces states at the Fermi level, indicating enhanced electrical conductivity and weakened metal‑hydrogen interactions that facilitate hydrogen release. In summary, these findings demonstrate the ability of this combined approach to optimize KMgH₃ for advanced hydrogen storage applications.

在这项研究中，我们利用结合密度泛函理论（DFT）和从头算分子动力学（AIMD）模拟的综合第一性原理方法研究了KMgH3的储氢潜力。我们研究了25%的钾被铝和钛（Al,Ti）取代的影响，引入钾空位，以及压缩应变对掺杂KMgH3结构的影响，重点研究了它们的结构稳定性、热力学、储氢和电子性能。结果表明，掺杂和空位工程都能显著降低生成焓和解吸温度，提高脱氢性能。值得注意的是，钾空位使重容量从4.55 wt%增加到5.33 wt%，而压缩应变使形成焓降至- 40.62 kJ/mol.H 2，解吸温度降至313.46 K。弹性常数分析和环境温度下的AIMD模拟证实了该系统在这些修改下具有结构稳定性和热稳定性。为了解释这种减少，我们检查了电子结构，结果表明钾原子的减少引入了费米能级的状态，表明电导率增强和金属氢相互作用减弱，促进了氢的释放。总之，这些发现证明了这种组合方法优化KMgH3用于先进储氢应用的能力。

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引用次数: 0

A new interatomic potential for mixed Mg-Al-Ga-In spinels 混合Mg-Al-Ga-In尖晶石的新原子间势

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-15 DOI: 10.1016/j.commatsci.2026.114492

Michael J.D. Rushton , Michael W.D. Cooper , Ghanshyam Pilania , Blas P. Uberuaga

While density functional theory (DFT) has become the de facto approach for accurate simulation of materials at the atomic scale, there are many aspects of materials that are simply out of reach of DFT methods. In particular, finite temperature properties such as diffusivities, the structure and properties of grain boundaries and interfaces, and the study of defect properties in complex alloys are computationally challenging for DFT methods. Recently, a new class of spinels in which three cations order over two sublattices was discovered. In order to predict the properties of these types of structures, classical potentials are a must. In this work, we derive a new classical potential for Mg-bearing spinels in which the B cations are Al, Ga, and/or In. The potential does well in describing the DFT energetics of various spinel structures as a function of chemistry and inversion. In particular, it reproduces the thermodynamically favorable MgAlGaO₄ structure while correctly predicting that neither MgAlInO₄ nor MgGaInO₄ are stable. Further, it reproduces physical trends in elastic properties as compared against experiment.

虽然密度泛函理论（DFT）已经成为在原子尺度上精确模拟材料的事实上的方法，但材料的许多方面是DFT方法无法达到的。特别是，有限的温度性质，如扩散系数、晶界和界面的组织和性质，以及复杂合金缺陷性质的研究，对DFT方法的计算具有挑战性。最近，发现了一类新的尖晶石，其中三个阳离子在两个亚晶格上有序。为了预测这类结构的性质，经典势是必须的。在这项工作中，我们推导了含镁尖晶石的一个新的经典势，其中B阳离子是Al， Ga和/或In。势很好地描述了各种尖晶石结构的DFT能量学作为化学和反转的函数。特别是，它再现了热力学有利的MgAlInO4结构，同时正确地预测了MgAlInO4和MgGaInO4都不是稳定的。此外，与实验相比，它再现了弹性性能的物理趋势。

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引用次数: 0

Bayesian discovery of optimal reduced order models from mechanistic and experimental data: A case study of Pd penetration in TRISO fuels using BISON 从力学和实验数据中贝叶斯发现最优降阶模型：使用BISON对三iso燃料中Pd渗透的案例研究

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-15 DOI: 10.1016/j.commatsci.2026.114503

Chaitanya Bhave, Somayajulu L.N. Dhulipala, Mathew Swisher, Jacob A. Hirschhorn, Ryan Terrence Sweet, Stephen R. Novascone

TRistructural ISOtropic (TRISO) particle fuel relies on a silicon carbide (SiC) layer as the primary structural material and barrier to metallic fission products (FPs) release. Accurate prediction of palladium (Pd) transport and penetration into the SiC is therefore critical for qualifying TRISO fuels for advanced reactors. The empirical correlation for Pd penetration in BISON is derived from historical particle-fuel data, but it cannot explain the large scatter in the experimental data that arises from varying experimental conditions. To aid fuel qualification, we previously developed a mechanistic reduced order model (ROM) using BISON that resolves these dependencies (Bhave et al., 2025). In this work we built on that mechanistic ROM, validated it, and quantified its uncertainty using Bayesian uncertainty quantification (UQ). We calibrated against a suite of in-pile and out-of-pile experiments spanning particle compositions, geometries, and operating conditions, and benchmarked the mechanistic ROM against the empirical correlation. We used Bayesian UQ to identify influential parameters and calibrate them to data, which yielded predictive intervals. Results show that while the empirical correlation can be tuned to fit a single experiment type, it transfers poorly; the mechanistic ROM sustains accuracy with credible uncertainty across disparate conditions. This process demonstrates a practical path — via Bayesian UQ applied to mechanistic ROMs — to leverage single-effect experiments for inferring in-reactor behavior and supporting TRISO fuel qualification.

三结构各向同性（TRISO）粒子燃料依靠碳化硅（SiC）层作为主要结构材料和金属裂变产物（FPs）释放的屏障。因此，准确预测钯（Pd）在碳化硅中的传输和渗透对于先进反应堆的TRISO燃料的资格至关重要。BISON中Pd渗透的经验相关性来源于历史颗粒-燃料数据，但它不能解释实验数据中由于不同实验条件而产生的大分散。为了帮助燃料鉴定，我们之前使用BISON开发了一种机制降order模型（ROM）来解决这些依赖关系（Bhave et al., 2025）。在这项工作中，我们建立了机械ROM，验证了它，并使用贝叶斯不确定性量化（UQ）量化了它的不确定性。我们根据一套桩内和桩外实验进行了校准，涵盖了颗粒组成、几何形状和操作条件，并根据经验相关性对机械ROM进行了基准测试。我们使用贝叶斯UQ来识别有影响的参数，并将其校准为数据，从而产生预测区间。结果表明，虽然经验相关性可以调整到适合单一实验类型，但它的转移性很差；机械式只读存储器在不同的条件下保持具有可靠的不确定性的准确性。该过程展示了一种实用的途径——通过将贝叶斯UQ应用于机械rom——利用单效应实验来推断反应堆内行为并支持TRISO燃料鉴定。

{"title":"Bayesian discovery of optimal reduced order models from mechanistic and experimental data: A case study of Pd penetration in TRISO fuels using BISON","authors":"Chaitanya Bhave, Somayajulu L.N. Dhulipala, Mathew Swisher, Jacob A. Hirschhorn, Ryan Terrence Sweet, Stephen R. Novascone","doi":"10.1016/j.commatsci.2026.114503","DOIUrl":"10.1016/j.commatsci.2026.114503","url":null,"abstract":"<div><div>TRistructural ISOtropic (TRISO) particle fuel relies on a silicon carbide (SiC) layer as the primary structural material and barrier to metallic fission products (FPs) release. Accurate prediction of palladium (Pd) transport and penetration into the SiC is therefore critical for qualifying TRISO fuels for advanced reactors. The empirical correlation for Pd penetration in BISON is derived from historical particle-fuel data, but it cannot explain the large scatter in the experimental data that arises from varying experimental conditions. To aid fuel qualification, we previously developed a mechanistic reduced order model (ROM) using BISON that resolves these dependencies (Bhave et al., 2025). In this work we built on that mechanistic ROM, validated it, and quantified its uncertainty using Bayesian uncertainty quantification (UQ). We calibrated against a suite of in-pile and out-of-pile experiments spanning particle compositions, geometries, and operating conditions, and benchmarked the mechanistic ROM against the empirical correlation. We used Bayesian UQ to identify influential parameters and calibrate them to data, which yielded predictive intervals. Results show that while the empirical correlation can be tuned to fit a single experiment type, it transfers poorly; the mechanistic ROM sustains accuracy with credible uncertainty across disparate conditions. This process demonstrates a practical path — via Bayesian UQ applied to mechanistic ROMs — to leverage single-effect experiments for inferring in-reactor behavior and supporting TRISO fuel qualification.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"264 ","pages":"Article 114503"},"PeriodicalIF":3.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning detection of topological defects in confined two-dimensional nematics 受限二维向列图拓扑缺陷的深度学习检测

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-14 DOI: 10.1016/j.commatsci.2026.114508

Ignacio Palos-Reynoso , Humberto Híjar

We present a neural network-based algorithm for the identification and classification of topological defects in two-dimensional nematic liquid crystals confined within square geometries. The nematic configurations are generated through Nematic-Multiparticle Collision Dynamics, a mesoscopic simulation method that captures both hydrodynamic and orientational fluctuations. Our supervised learning framework is trained on synthetic images labeled with topological defects of positive and negative charge. In the inference stage, we employ local winding number estimations to propose candidate defect locations, which are then evaluated by the neural network to determine their authenticity and type. The algorithm achieves robust classification performance, with a macro-averaged

F_{1}

score of 0.92, indicating balanced precision and recall across all defect classes, with most misclassifications arising from director field fluctuations near the corners of the confinement domain. Beyond static identification, our method enables the temporal tracking of defect dynamics, including annihilation events. This work demonstrates the potential of deep learning tools to extract and quantify topological information in fluctuating soft matter systems.

提出了一种基于神经网络的二维向列液晶拓扑缺陷识别与分类算法。向列型结构是通过向列多粒子碰撞动力学生成的，这是一种介观模拟方法，可以捕获流体力学和方向波动。我们的监督学习框架是在带有正负电荷拓扑缺陷的合成图像上进行训练的。在推理阶段，我们使用局部圈数估计来提出候选缺陷位置，然后通过神经网络对候选缺陷位置进行评估以确定其真实性和类型。该算法具有鲁棒的分类性能，宏观平均F1分数为0.92，表明所有缺陷类别的精度和召回率平衡，大多数错误分类是由约束域角附近的方向场波动引起的。除了静态识别之外，我们的方法还支持缺陷动态的时间跟踪，包括湮灭事件。这项工作证明了深度学习工具在波动软物质系统中提取和量化拓扑信息的潜力。

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引用次数: 0

Electrides: From fundamental concepts to tunable magnetism in layered systems 电子：从基本概念到层状系统中的可调谐磁性

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-13 DOI: 10.1016/j.commatsci.2026.114505

Mary A. Mazannikova , Vladimir I. Anisimov , Dmitry Y. Novoselov

Layered electrides, characterized by anionic electrons confined in interstitial sites, present a unique platform for engineering exotic electronic and magnetic phenomena. This study employs a combination of density functional theory, maximally localized Wannier functions, and dynamical mean-field theory to systematically investigate the emergence and control of magnetism in a family of twelve isostructural

M_{2} X

electrides (M

=

Ca, Sr, Ba; X

=

N, P, As, Sb). We demonstrate that the magnetic state is governed by the local geometry of the interstitial cavities, specifically by the ratio of intra- to inter-layer metal–metal distances (

l_{intra} / l_{inter}

). A magnetic ground state emerges when this ratio falls below unity, a condition that can be selectively induced by hydrostatic pressure. Electronic structure analysis reveals that this transition is driven by a Stoner-like instability, associated with the flattening of an electride-derived band at the Fermi level. Our DMFT calculations confirm the presence of significant electron correlations and spin fluctuations near the magnetic instability, indicative of a correlated metallic state. The strong coupling between magnetic ordering and the crystal lattice, evidenced by concurrent structural and magnetic phase transitions, underscores a robust magneto-structural coupling. We establish simple empirical criteria based on atomic radii and electronegativities to predict magnetic behavior within this family of compounds. These findings provide a comprehensive microscopic understanding of magnetism in layered electrides and establish design principles for creating and tuning magnetic materials via pressure or chemical substitution from non-magnetic elements.

层状电子，其特征是阴离子电子被限制在间隙位置，为工程奇异的电子和磁现象提供了一个独特的平台。本研究采用密度泛函理论、最大定域万涅尔函数和动力学平均场理论相结合的方法，系统地研究了12种M2X等结构电子（M = Ca, Sr, Ba; X = N， P, As, Sb）中磁性的产生和控制。我们证明了磁性状态是由间隙腔的局部几何形状控制的，特别是由层内与层间金属-金属距离的比率（lintra/linter）控制的。当这个比率低于1时，磁性基态就会出现，这种情况可以由静水压力选择性地诱导。电子结构分析表明，这种转变是由一种类似斯通纳的不稳定性驱动的，这种不稳定性与费米能级上电极衍生带的平坦化有关。我们的DMFT计算证实了磁不稳定性附近存在显著的电子相关性和自旋波动，表明存在相关的金属态。磁有序与晶格之间的强耦合，通过同时发生的结构和磁相变证明，强调了强磁-结构耦合。我们建立了基于原子半径和电负性的简单经验准则来预测这类化合物的磁性行为。这些发现为层状电子中的磁性提供了全面的微观理解，并建立了通过压力或非磁性元素的化学替代来创建和调整磁性材料的设计原则。

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引用次数: 0

Molecular dynamic simulation study on 3MOAl2O3 3SiO2 [M = Ba, Sr, Ca, Mg, Zn and Mn] glasses 3MOAl2O3 3SiO2 [M = Ba, Sr, Ca, Mg， Zn和Mn]玻璃的分子动力学模拟研究

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-13 DOI: 10.1016/j.commatsci.2026.114486

Veeramohan Rao M

Aluminosilicate glasses incorporating alkaline earth metals, Zn, and Mn are of significant interest within the realms of material science and geoscience. The structural implications of the MO/Al₂O₃ ratio, specifically at a value of 3, in aluminosilicate glasses remain incompletely elucidated. In the present study, molecular dynamic simulations are employed to investigate the influence of cation field strength on the structural and elastic characteristics of aluminosilicate glasses. The cation field strength is observed to induce alterations in structural properties attributes such as bond length, coordination number, bond angle distribution, and the presence of oxygen species including FO, NBO, BO, and TBO. The glass transition temperature and elastic constants are determined through these simulations. It is found that an increase in cation field strength correlates with an elevation in elastic constants and a reduction in glass transition temperature. These findings provide atomic-scale insights into the effects of cation field strength on the properties of glasses.

含有碱土金属、锌和锰的铝硅酸盐玻璃在材料科学和地球科学领域具有重要意义。MO/Al2O3比在铝硅酸盐玻璃中的结构意义，特别是当其值为3时，仍未完全阐明。本文采用分子动力学模拟方法研究了阳离子场强度对铝硅酸盐玻璃结构和弹性特性的影响。观察到阳离子场强会引起结构属性的改变，如键长、配位数、键角分布以及氧（包括FO、NBO、BO和TBO）的存在。通过这些模拟确定了玻璃化转变温度和弹性常数。发现阳离子场强的增加与弹性常数的升高和玻璃化转变温度的降低有关。这些发现提供了原子尺度上对阳离子场强对玻璃性质影响的见解。

引用次数: 0

Development and validation of interatomic potential for Sc and Al–Sc alloys: Thermodynamics, solidification, and intermetallic ordering Sc和Al-Sc合金原子间势的发展和验证：热力学、凝固和金属间有序

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-13 DOI: 10.1016/j.commatsci.2025.114443

Avik Mahata

We present a second-nearest-neighbor Modified Embedded Atom Method (2NN–MEAM) potential for Scandium (Sc) and Aluminum-Scandium (Al–Sc) alloys that unifies cohesive, thermodynamic, and solidification behavior within a single transferable framework. The Sc component accurately reproduces cohesive energy, lattice constants, defect energetics, and the experimental melting point obtained from two-phase coexistence, demonstrating reliable description of both hcp and liquid phases. The Al–Sc binary interaction parameters were fitted using the L1

_{2}

–Al

_{3}

Sc reference and benchmarked against first-principles and calorimetric data. The potential reproduces the strong negative formation enthalpy of Al

_{3}

Sc (–0.45 eV atom⁻¹), correct relative stability of competing phases, and realistic elastic properties. Mixing enthalpies of the liquid alloy agree with ideal-associated-solution and CALPHAD models, confirming that the potential captures exothermic Al–Sc association in the melt. Molecular-dynamics simulations of solidification reveal the expected temperature and composition dependence of homogeneous nucleation. Pure Al crystallizes readily, while Al–1 at.% Sc exhibits a longer incubation and slower growth at the same absolute temperature due to reduced undercooling and solute drag. Within the alloy, ordered Al

_{3}

Sc-type L1

_{2}

embryos appear spontaneously, with Sc atoms occupying cube-corner (B) sites surrounded by twelve Al neighbors. Energy–volume trajectories confirm that the potential links thermodynamics to microstructural evolution. Overall, the developed 2NN–MEAM potential provides a quantitatively grounded basis for modeling melting, solidification, and intermetallic ordering in Sc and Al–Sc systems, enabling future multicomponent alloy design and large-scale nucleation studies.

我们提出了钪（Sc）和铝-钪（Al-Sc）合金的第二近邻修正嵌入原子法（2NN-MEAM）潜力，该方法在一个可转移的框架内统一了内聚、热力学和凝固行为。Sc组分精确地再现了从两相共存得到的内聚能、晶格常数、缺陷能量和实验熔点，证明了对hcp和液相的可靠描述。采用L12-Al3Sc标准拟合Al-Sc二元相互作用参数，并以第一性原理和量热数据为基准。该势再现了Al3Sc （-0.45 eV原子−1）的强负生成焓，正确的竞争相相对稳定性和真实的弹性性质。液态合金的混合焓与理想相关溶液和CALPHAD模型一致，证实了该势捕获了熔体中的放热Al-Sc结合。凝固的分子动力学模拟揭示了均匀形核的预期温度和成分依赖性。纯铝容易结晶，而Al - 1不易结晶。在相同的绝对温度下，由于过冷和溶质阻力的减少，Sc的培养时间更长，生长速度较慢。在合金内部，有序的al3sc型L12胚胎自发出现，Sc原子占据立方体角(B)的位置，周围环绕着12个Al邻居。能量-体积轨迹证实了热力学与微观结构演化之间的潜在联系。总的来说，开发的2NN-MEAM潜力为Sc和Al-Sc体系的熔化、凝固和金属间有序建模提供了定量基础，使未来的多组分合金设计和大规模成核研究成为可能。

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引用次数: 0