Computational Materials Science最新文献_第7页

Atomistic analysis of dynamic shock compression behavior of VNbTiTaZr refractory high-entropy alloy VNbTiTaZr难熔高熵合金动态冲击压缩行为的原子分析

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

Computational Materials Science

Pub Date : 2025-12-15 DOI: 10.1016/j.commatsci.2025.114449

Mehady Hassan , Mohammad Jane Alam Khan

The thermal stability, excellent mechanical property, and low cost marks VNbTaTiZr refractory high-entropy alloy (RHEA) as a favorable material for applications in extreme conditions. However, its deformation behavior under high strain rates is mostly unexplored. This study performs non-equilibrium molecular dynamics (NEMD) simulations to investigate the dynamic shock response of VNbTaTiZr RHEA. Three crystallographic directions [0 0 1], [1 1 0], and [1 1 1], are analyzed for different piston velocities and temperatures in the range of 0.4–1.6 km/s and 100–1200 K, respectively. We have observed that shock response depends strongly on shock direction and strength, and temperature. Deformation modes are found to be as dislocation slip, twinning, and phase transformations. [1 1 0] direction has the highest dislocation activity while [0 0 1] direction demonstrates highest spall strength and resistance to plastic flow. [1 1 1] direction shows some delay in the onset of plastic flow and has the lowest elastic stability and spall strength. Shock properties, i.e., Hugoniot elastic limit (HEL), Hugoniot stress, and shock velocity increases and spall strength decreases with increasing piston velocity. All the shock properties decrease with increasing initial temperature due to thermal softening. Hugoniot stress is comparable while shear stress varies in different shock directions, indicating isotropic equilibrium shock response and anisotropic deformation mechanisms. These findings are crucial for optimizing VNbTiTaZr for high temperature and high strain rate applications.

VNbTaTiZr耐火高熵合金（RHEA）的热稳定性、优异的机械性能和低廉的成本使其成为极端条件下应用的理想材料。然而，其在高应变速率下的变形行为大多未被探索。本研究采用非平衡分子动力学（NEMD）模拟研究了VNbTaTiZr RHEA的动态冲击响应。在0.4 ~ 1.6 km/s和100 ~ 1200 K范围内，分别分析了不同活塞速度和温度下[0 0 1]、[1 100]和[1 1 1]三个晶体学方向。我们已经观察到，冲击响应在很大程度上取决于冲击方向、强度和温度。变形形式主要有位错滑移、孪晶和相变。[1 10]方向的位错活度最高，[0 0 1]方向的位错强度和抗塑性流动能力最高。[1 1 1]方向塑性流动的开始有一定的延迟，弹性稳定性和颗粒强度最低。冲击性能，即Hugoniot弹性极限（HEL）、Hugoniot应力和冲击速度随活塞速度的增加而增加，而小块强度随活塞速度的增加而降低。由于热软化作用，随着初始温度的升高，材料的冲击性能均下降。Hugoniot应力具有可比性，而剪切应力在不同冲击方向上存在差异，表明各向同性平衡冲击响应和各向异性变形机制。这些发现对于优化高温和高应变率应用的VNbTiTaZr至关重要。

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引用次数: 0

Unveiling the twinning and evolution of β precipitates in Al–Mg alloys: A phase field model 揭示Al-Mg合金中β析出相的孪生和演化：相场模型

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

Computational Materials Science

Pub Date : 2025-12-15 DOI: 10.1016/j.commatsci.2025.114450

Xitian Ou, Zhipeng Pi, Donglin Li, Sifeng Huang, Fan Zhang

This study proposes a unified multiphysics phase-field model to simulate the microstructural evolution in Al–Mg alloys, which involves two stages: first, the precipitation of the

β

phase (

β

-Al

_{3}

Mg

_{2}

) from the

α

phase (FCC Al), and second, the twinning deformation within the

β

phase after its growth to a critical size. This model integrates the CALPHAD thermodynamic database and fully considers the effects of anisotropic interface energies, including the

α / β

phase boundary and the twin boundaries, as well as the heterogeneous effects arising from the elastic misfit between the precipitate and matrix phases. Based on this, the KKS model is employed to model the

α

-

β

phase transformation; the deformation twinning phase field model is employed to describe the twinning process; two models are weakly coupled to reproduce the twinned precipitate microstructure. The simulated results are in excellent agreement with experimental data, validating the model’s effectiveness in characterizing the complex microstructural evolution of this type of alloy. The findings indicate that the nucleation and evolution of twinning in precipitates, a process limited by precipitate size, follow the Hall–Petch relation, providing a new theoretical framework for understanding phase transformation behaviors in Al–Mg alloys and their impact on material properties.

本研究提出了一种统一的多物理场相场模型来模拟Al - mg合金的微观组织演化过程，该过程包括两个阶段：一是β相（β- al3mg2）从α相（FCC Al）中析出，二是β相长大到临界尺寸后在β相内发生孪晶变形。该模型集成了CALPHAD热力学数据库，充分考虑了各向异性界面能的影响，包括α/β相边界和孪晶界，以及析出相与基体相弹性失配引起的非均质效应。在此基础上，采用KKS模型对α-β相变进行建模；采用变形孪晶相场模型来描述孪晶过程；两个模型弱耦合以重现孪晶析出组织。模拟结果与实验数据吻合良好，验证了该模型在表征该类合金复杂组织演变过程中的有效性。研究结果表明，析出相中孪晶的形核和演化遵循Hall-Petch关系，这一过程受析出相尺寸的限制，为理解Al-Mg合金的相变行为及其对材料性能的影响提供了新的理论框架。

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引用次数: 0

Interfacial bonding mechanisms in TiB2/Ti systems fabricated by molten salt electrophoretic deposition: Experimental and first-principles calculation 熔盐电泳制备TiB2/Ti体系的界面键合机制：实验和第一性原理计算

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

Computational Materials Science

Pub Date : 2025-12-14 DOI: 10.1016/j.commatsci.2025.114445

Peipei Liu, Chunchun Dong, Xiping Chen, Xuemin Liang

Superhard ceramics are suitable materials to improve the surface characteristics of titanium. In this study, TiB₂ nanopowder was coated on titanium substrate using electrophoretic deposition in NaF-AlF₃-Al₂O₃ molten salts. The effects of varying deposition voltages and deposition times on TiB₂ coatings were investigated. Additionally, the microstructure of the coating surface was observed by scanning electron microscopy. With an applied cell voltage of 1.2 V and a deposition time of 1.5 h, a dense, defect-free TiB₂/TiB composite coating (including the outer TiB₂ layer and the inner TiB whiskers layer) approximately 74 μm thick was produced, exhibiting a strong metallurgical bond to the substrate. A densely packed TiB whisker layer forms at the TiB₂/Ti interface. The work of adhesion, interfacial energy, and electronic structure of the α-Ti(0001)/TiB₂(0001) interfaces have been calculated using first-principles methods. Simultaneously, the diffusion of B atoms at the TiB₂/Ti interface was computed to explain the formation process of TiB whisker layers. The work of adhesion calculations indicated that the B-HCP-Ti interfacial model exhibited the highest interfacial bond strength. The electronic structure of the interfacial model was analyzed to show that the Ti-terminated interface model was primarily formed through metallic bonds, while the B-terminated interface model was primarily formed through covalent bonds. Compared to the Ti-terminated interface, the B-terminated interface facilitates the diffusion of boron from TiB₂ into the titanium substrate more effectively.

超硬陶瓷是改善钛表面特性的理想材料。在本研究中，采用NaF-AlF3-Al2O3熔盐电泳沉积的方法将TiB2纳米粉末涂覆在钛基上。研究了不同沉积电压和沉积时间对TiB2涂层的影响。此外，通过扫描电镜观察了涂层表面的微观结构。在电池电压为1.2 V、沉积时间为1.5 h的条件下，制备了厚度约为74 μm的致密、无缺陷的TiB2/TiB复合镀层（包括TiB2外晶须层和TiB内晶须层），与基体具有较强的冶金结合。在TiB2/Ti界面处形成致密的TiB晶须层。用第一性原理方法计算了α-Ti(0001)/TiB2（0001）界面的粘附功、界面能和电子结构。同时，计算了B原子在TiB2/Ti界面的扩散，解释了TiB晶须层的形成过程。结果表明，B-HCP-Ti界面模型的界面结合强度最高。界面模型的电子结构分析表明，端部为ti的界面模型主要通过金属键形成，端部为b的界面模型主要通过共价键形成。与ti端界面相比，b端界面能更有效地促进硼从TiB2向钛基体的扩散。

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引用次数: 0

Structures, stability and magnetic properties of small cobalt clusters on Al-doped Cu(111) surface: A DFT + U study al掺杂Cu（111）表面小钴团簇的结构、稳定性和磁性：DFT + U研究

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

Computational Materials Science

Pub Date : 2025-12-13 DOI: 10.1016/j.commatsci.2025.114442

Zihao Zhao , Mingwei Li , Ping Wu , Zijie Huang , Ying Zhang , Min Huang

The atomic structures, stability and magnetic properties of small Co_n (n = 1–6) clusters on Al-doped Cu(111) surface were investigated by means of Density Functional Theory calculations (DFT + U) and ab initio molecular dynamics (AIMD). Our calculation results indicate that the supported clusters considered are all in two-dimensional structures, which show high stability at temperature up to 600 K evidenced by our AIMD simulations. Furthermore, the relative stability of small Co clusters on Al-doped Cu(111) surface enhances with the increasing of cluster size. Therefore, 1 monolayer (ML) Co film show high stability on Al-doped Cu(111) surface, which is in agreement with experimental observations and high quality Co film may be deposited on Al-doped Cu(111) surface for device applications. Our calculation suggests that AlCu alloy buffer layer only have significant effects on Co film growth at initial stage since the adsorption sites for Co_n (n = 1–5) clusters are different on Al-doped Cu(111) surface (bridge site of two neighboring surface Al atoms) and Cu(111) pristine surface (hcp hollow site), and the adsorption sites of Co_n (n = 6–13) clusters become the same on both surfaces. All the considered systems of Al-doped Cu(111) surface with Co_n cluster adsorbed show ferromagnetic ground states with high magnetic moments and the spin density distributions mainly origin from Co_n clusters and surface Cu and Al atoms have very minor contributions. Co ML film on Al-doped Cu(111) surface exhibits preference of in-plane magnetization, which agrees the behavior of Co films on Cu(111) surface prepared by pulsed laser ablation. Our study can provide basic information of the initial stages of Co film growth and nucleation on Al-doped Cu(111) substrate at atomic level and may shed light on Co/Al-Cu(111) system being potential applications in spintronic devices.

利用密度泛函理论（DFT + U）和从头算分子动力学（AIMD）研究了al掺杂Cu（111）表面Con （n = 1-6）簇的原子结构、稳定性和磁性。我们的计算结果表明，所考虑的支持团簇都是二维结构，在高达600 K的温度下具有很高的稳定性，我们的AIMD模拟证明了这一点。此外，小Co团簇在al掺杂Cu（111）表面的相对稳定性随着团簇尺寸的增大而增强。因此，1单层（ML） Co膜在al掺杂Cu（111）表面表现出较高的稳定性，这与实验观察结果一致，可以在al掺杂Cu（111）表面沉积高质量的Co膜，用于器件应用。我们的计算表明，由于Con （n = 1-5）团簇在Al掺杂Cu（111）表面（两个相邻表面Al原子的桥位）和Cu（111）原始表面（hcp空心位）的吸附位置不同，而Con （n = 6-13）团簇在这两个表面的吸附位置趋于一致，因此AlCu合金缓冲层仅在初始阶段对Co膜的生长有显著影响。所考虑的含有Con团簇的掺铝Cu（111）表面体系均表现出高磁矩的铁磁基态，自旋密度分布主要来源于Con团簇，表面Cu和Al原子的贡献很小。掺杂al的Cu（111）表面的Co ML膜表现出面内磁化的偏好，这与脉冲激光烧蚀制备的Cu（111）表面Co膜的行为一致。本研究提供了在掺杂al的Cu（111）衬底上初始阶段Co膜生长和成核的基本信息，并为Co/Al-Cu（111）体系在自旋电子器件中的潜在应用提供了线索。

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引用次数: 0

Double-fold effect of vacancies and interstitials on the migration of Σ3{112} incoherent twin boundary in copper 空位和间隙对铜中Σ3{112}非相干孪晶界迁移的双重影响

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

Computational Materials Science

Pub Date : 2025-12-13 DOI: 10.1016/j.commatsci.2025.114435

Daqi Yuan , Tingting He , Hongxian Xie , Huigang Shi , Xianglong Guo , Guang-Hong Lu

The migration of Σ3{112} incoherent twin boundary (ITB) in nanotwinned face-centered cubic metals/alloys can lead to twinning or detwinning, thereby significantly influencing the stability of the nanotwinned structure; however, the effect of radiation-induced defects on the migration of the ITB is not fully understood and needs further investigation. In the present study, the effect of vacancies and interstitials on the migration of the ITB in Cu was systematically studied by molecular dynamic simulation method. The simulation results revealed that vacancies/interstitials have double-fold effect on the migration of the ITB: resistance effect works during absorption process of vacancies/interstitials into the ITB, while the absorbed vacancies/interstitials can accelerate rather than retard the migration of the ITB. Importantly, the resistance effect plays a dominant role, thus the migration of ITB can be retarded by radiation-induced defects. Furthermore, the underlying mechanism of the double-fold effect was discussed that cross-layer rearrangement of vacancies in the ITB is prevailing in retarded effect on the ITB migration. The present work gives a deeper insight into the effect of radiation-induced defects on the migration of ITB.

纳米孪晶面心立方金属/合金中Σ3{112}非共格孪晶界（ITB）的迁移可导致孪晶或去孪晶，从而显著影响纳米孪晶结构的稳定性；然而，辐射缺陷对ITB迁移的影响尚不完全清楚，需要进一步研究。本研究采用分子动力学模拟的方法，系统研究了空位和间隙对铜中ITB迁移的影响。模拟结果表明，空位/间隙对过渡段的迁移具有双重影响：空位/间隙在过渡段吸收过程中起阻力作用，而被吸收的空位/间隙能加速过渡段的迁移，而不是阻碍过渡段的迁移。重要的是，电阻效应起主导作用，因此辐射诱导缺陷可以延缓ITB的迁移。进一步探讨了双重效应的潜在机制，即在阻滞ITB迁移的过程中，ITB中空位的跨层重排是主要的。本研究对辐射诱导缺陷对ITB迁移的影响有了更深入的了解。

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引用次数: 0

Revealing the dynamic evolution of the electronic structure at the Ti2AlC/TiAl interface during tensile deformation by the first-principles calculations 通过第一性原理计算揭示了拉伸变形过程中Ti2AlC/TiAl界面电子结构的动态演变

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

Computational Materials Science

Pub Date : 2025-12-13 DOI: 10.1016/j.commatsci.2025.114444

Hongwu Gao , Hangyu Yue , Yinwen Mao , Rengeng Li

Interfaces serve as critical bridges between the matrix and the reinforcement phase, significantly influencing the comprehensive properties of TiAl-based composites. First-principles computational tensile tests combined with crystal orbital Hamilton population (COHP) and electron localization function (ELF) analyses were employed to systematically investigate the evolution of the electronic structure at the Ti₂AlC(0001)/TiAl(111) interface during tensile deformation. The strength variations of chemical bonds at the interface—such as TiAl covalent bonds and TiTi metallic bonds—are quantitatively analyzed, and the bond strength is evaluated using the integrated COHP (ICOHP). The results indicated that the critical interfacial fracture strain was 19.5 %, and the theoretical tensile strength was 14.71 GPa. ICOHP analysis revealed that the strengths of the interfacial TiAl and TiTi bonds decreased by 0.471 eV and 0.597 eV at a strain of 18 %, respectively. The -ICOHP values of all interfacial bonds remained positive throughout the deformation process, with the TiAl bond being the strongest (initial -ICOHP = 1.295 eV). This study reveals the fracture mechanism of the Ti₂AlC/TiAl interface at the electronic scale, providing theoretical guidance for the design and application of such composites.

界面是连接基体与增强相的关键桥梁，对tial基复合材料的综合性能有重要影响。采用第一性原理计算拉伸试验，结合晶体轨道Hamilton族（COHP）和电子定位函数（ELF）分析，系统地研究了拉伸变形过程中Ti2AlC(0001)/TiAl（111）界面电子结构的演变。定量分析了界面上化学键（TiAl共价键和钛金属键）的强度变化，并用综合COHP （ICOHP）评价了键的强度。结果表明：合金的临界界面断裂应变为19.5%，理论抗拉强度为14.71 GPa；ICOHP分析表明，当应变为18%时，界面TiAl键和tii键的强度分别下降了0.471 eV和0.597 eV。在整个变形过程中，所有界面键的-ICOHP值都保持为正，其中TiAl键的-ICOHP最强（初始值为1.295 eV）。本研究揭示了Ti2AlC/TiAl界面在电子尺度上的断裂机理，为该类复合材料的设计和应用提供了理论指导。

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引用次数: 0

Machine learning-driven prognostication of multifunctional properties of (K,Na)NbO3-based lead-free ceramics for optimized materials design 基于机器学习的（K,Na） nbo3基无铅陶瓷多功能性能预测优化材料设计

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

Computational Materials Science

Pub Date : 2025-12-13 DOI: 10.1016/j.commatsci.2025.114437

Manisha Kumari, Alok Shukla

Potassium sodium niobate (KNN)-based lead-free ceramics are prime prospects for eco-friendly dielectric and piezoelectric technology, and their multi-functionality properties, such as dielectric constant (ε_r), Curie temperature (T_c), and high piezoelectric coefficient (d₃₃), make them promising materials for next-generation devices. Recent advancements in machine learning (ML) offer the possibility of accelerating materials discovery by prognosticating such properties directly from composition and processing conditions. But current studies are often limited by small datasets, narrow descriptor sets, or a lack of interpretability, leading to models with poor generalization and limited practical utility for guiding experimental synthesis. Therefore, this study developed a single ML framework trained on a large literature-based dataset, utilizing five feature spaces such as compositional, chemical, processing, microstructural, and structural descriptors to estimate d₃₃, ε_r, and T_c of KNN-based ceramics. Six different ML algorithms were trained and tested for the prediction of each property, and their performance was optimized using a randomized search and a 10-fold cross-validation technique. Among all the developed ML models, the extreme gradient boosting algorithm showed the highest performance with R² values of 0.93, 0.9781, and 0.9504 for d₃₃, ε_r, and T_c, respectively, on the test set. For better interpretability of the model, the Shapley Additive Explanation methodology was employed, and it identified and explained the most significant features that contributed to the target performance. Model validity was confirmed by synthesizing compositions withheld from the training data and comparing predictions with the experimental measurements, which exhibited close agreement. This approach offers an interpretable and scalable path to the rapid design of high-performance lead-free dielectric ceramics with verification against data reported in the literature.

铌酸钾钠（KNN）基无铅陶瓷是环保介质和压电技术的主要应用领域，其介电常数（εr）、居里温度（Tc）和高压电系数（d33）等多功能特性使其成为下一代器件的理想材料。机器学习（ML）的最新进展提供了通过直接从成分和加工条件预测这些特性来加速材料发现的可能性。但目前的研究往往受到小数据集、狭窄描述符集或缺乏可解释性的限制，导致模型泛化能力差，指导实验综合的实际效用有限。因此，本研究开发了一个基于大型文献数据集训练的单一ML框架，利用成分、化学、加工、微观结构和结构描述符等五个特征空间来估计knn基陶瓷的d33、εr和Tc。我们对六种不同的ML算法进行了训练和测试，以预测每种属性，并使用随机搜索和10倍交叉验证技术对其性能进行了优化。在所有开发的ML模型中，极端梯度增强算法在测试集上的d33、εr和Tc的R2分别为0.93、0.9781和0.9504，表现出最高的性能。为了更好地解释模型，采用了Shapley加性解释方法，它确定并解释了对目标性能有贡献的最重要的特征。通过综合训练数据中保留的成分，并将预测结果与实验测量结果进行比较，证实了模型的有效性。这种方法为高性能无铅介电陶瓷的快速设计提供了一种可解释和可扩展的途径，并对文献中报道的数据进行了验证。

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引用次数: 0

Band gap modulation by strain tuning in half-metallic KVTe 半金属KVTe中应变调谐带隙调制

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

Computational Materials Science

Pub Date : 2025-12-11 DOI: 10.1016/j.commatsci.2025.114438

Yashi Jain, Rajnish Kurchania

The rapid emergence of half-metallic materials has spurred accelerated research in materials science, opening the door to novel spintronic applications. The present study systematically explores the structural, magnetic, lattice-dynamical, strain-modulated electronic and transport characteristics of the half-Heusler chalcogenide KVTe using first-principles calculations. The structural, mechanical and thermodynamic stability of the material is analysed through elastic and phonon calculations. The strain-based electronic structure reveals half-metallic ferromagnetic nature of material, with a bandgap of 2.21 eV - 3.53 eV in minority spin state. Electronic properties reveal 100 % spin polarization and a high Curie temperature of 971 K. The figure of merit (zT) is 0.91 and 0.93 for the majority and minority channels, respectively. A high value of zT in both spin states reveals the outstanding thermoelectric efficiency of KVTe. Overall, it finds usefulness in spintronic and thermoelectric devices. It also offers band-gap modulation, suggesting its application in band-gap engineering.

半金属材料的迅速出现加速了材料科学的研究，打开了新型自旋电子应用的大门。本文采用第一性原理计算系统地探讨了半赫斯勒硫化物KVTe的结构、磁性、晶格动力学、应变调制电子和输运特性。通过弹性和声子计算分析了材料的结构、力学和热力学稳定性。基于应变的电子结构显示了材料的半金属铁磁性，在少数自旋状态下带隙为2.21 eV - 3.53 eV。电子特性显示出100%的自旋极化和971 K的高居里温度。多数通道和少数通道的优值（zT）分别为0.91和0.93。两个自旋态的高zT值表明KVTe具有出色的热电效率。总的来说，它在自旋电子和热电器件中很有用。它还提供带隙调制，提示其在带隙工程中的应用。

引用次数: 0

High-pressure behavior of energetic metal-organic frameworks: A first-principles study 高能金属有机骨架的高压行为：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 : 2025-12-10 DOI: 10.1016/j.commatsci.2025.114440

Mustafa Erkartal

Exploring how energetic materials respond to compression provides fundamental insight into their stability, reactivity, and design of safer high-performance formulations. This study presents the first investigation of energetic metal-organic frameworks (EMOFs) under hydrostatic pressure. Periodic DFT-D calculations were performed to probe the structural, mechanical, and electronic properties of four 3D EMOFs with experimentally characterized crystal structures. Calculations included full lattice relaxation under pressures of 0–30 GPa, elastic tensor analysis to determine bulk and shear moduli, Hirshfeld surface and two-dimensional fingerprint analyses to examine intermolecular interactions, and electronic structure calculations to obtain pressure-dependent band gaps. Structural and electronic data were then correlated with literature-reported detonation and sensitivity values to elucidate the mechanisms linking lattice motifs, elastic compliance, and electronic stability to energetic behavior. The results reveal that hinge-like deformation, anisotropic elasticity, and pressure-stable band gaps enhance structural resilience, whereas pronounced band gap collapse under compression increases electronic polarizability and sensitivity. These insights provide a foundation for rational design and high-pressure exploration of EMOFs.

探索高能材料如何对压缩作出反应，为其稳定性、反应性和更安全的高性能配方设计提供了基本的见解。本研究首次对静水压力下的含能金属-有机骨架（EMOFs）进行了研究。通过周期性的DFT-D计算来探测四种具有实验表征晶体结构的3D EMOFs的结构、力学和电子特性。计算包括0-30 GPa压力下的全晶格弛豫，弹性张量分析以确定体积和剪切模量，Hirshfeld表面和二维指纹分析以检查分子间相互作用，以及电子结构计算以获得与压力相关的带隙。然后将结构和电子数据与文献报道的爆炸和灵敏度值相关联，以阐明将晶格基序、弹性顺应性和电子稳定性与能量行为联系起来的机制。结果表明，类铰链变形、各向异性弹性和压力稳定带隙增强了结构的弹性，而压缩下明显的带隙塌陷增加了电子极化率和灵敏度。这些见解为EMOFs的合理设计和高压探索提供了基础。

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引用次数: 0

Multitask learning via task embeddings for glass property prediction with improved sample efficiency 基于任务嵌入的玻璃性能预测多任务学习，提高了样品效率

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

Computational Materials Science

Pub Date : 2025-12-10 DOI: 10.1016/j.commatsci.2025.114407

Gregor Maier , Jan Hamaekers , Benedikt Ziebarth

Glassy materials are prevalent in many modern-day applications, which place ever new demands on the materials’ properties. However, because of their amorphous nature, the design of glasses with specific properties is inherently difficult and necessarily data-driven. Due to the large size of the compositional space and the cost of the manufacturing process, glass data is only sparsely available. It is thus important to design and train models which maximize the benefit of each individual training sample on the models’ resulting prediction quality. In this paper, we study a multitask learning approach to meet this challenge. We analyze its effect on the sample efficiency, that is, the dependence of the test error on the training set size of a neural network model for predicting properties of oxide glasses. In contrast to existing encoder-based multitask learning models in computational glass science, we propose a decoder-based model, consisting of task embeddings in the input layer and a subsequent shared network architecture. This allows us to apply well-established single-task models in a multitask learning setting. We demonstrate in a series of numerical experiments, predicting the Young’s modulus and the log-viscosity, respectively, that this approach notably improves the model’s sample efficiency. To deal with parametric target properties, we complement the task embeddings by a parameter embedding and show that this preserves the advantageous multitask learning effect. In all experiments, we observe an algebraic scaling law for the decay of the test error with respect to the number of training samples.

玻璃材料在许多现代应用中普遍存在，这对材料的性能提出了新的要求。然而，由于它们的无定形性质，具有特定性能的玻璃的设计本身就很困难，并且必须由数据驱动。由于构成空间的大尺寸和制造过程的成本，玻璃数据只有稀疏可用。因此，重要的是设计和训练模型，使每个单独的训练样本对模型的预测质量产生最大的好处。在本文中，我们研究了一种多任务学习方法来应对这一挑战。我们分析了它对样本效率的影响，即用于预测氧化玻璃性质的神经网络模型的测试误差与训练集大小的依赖关系。与计算玻璃科学中现有的基于编码器的多任务学习模型相比，我们提出了一个基于解码器的模型，该模型由输入层的任务嵌入和随后的共享网络架构组成。这使我们能够在多任务学习环境中应用成熟的单任务模型。我们在一系列数值实验中分别预测了杨氏模量和对数黏度，证明了这种方法显著提高了模型的采样效率。为了处理参数目标属性，我们用参数嵌入来补充任务嵌入，并表明这保留了有利的多任务学习效果。在所有实验中，我们观察到测试误差随训练样本数量衰减的代数标度定律。

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