Computational Materials Science最新文献_第8页

Adjustable type-II band alignment of ferroelectric In2Se3/SnSe2 van der Waals heterostructures using strain: outstanding electronic, optical and photocatalytic properties 铁电In2Se3/SnSe2范德华异质结构的应变可调ii型带对准：突出的电子、光学和光催化性能

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

Computational Materials Science

Pub Date : 2026-03-10 Epub Date: 2026-02-17 DOI: 10.1016/j.commatsci.2026.114559

Dahua Ren , Qingwei Wang , Zhangyang Zhou , Xinguo Yan , Chunyan Zhang , Teng Zhang , Liushun Wang , Qiang Li , Xingyi Tan , Jinqiao Yi

In recent years, ferroelectric materials capable of reversible self-polarization have garnered considerable interest due to their potential for pioneering advancements in non-volatile memory and adaptive electronic devices. The vertically stacked In₂Se₃/SnSe₂ van der Waals (vdW) heterostructures composed of ferroelectric In₂Se₃ and InSe₂ were constructed, as elucidated by first-principles calculations within the density functional theory (DFT). The results show that two heterostructures exhibit type-II band alignments, which are favorable for photocatalytic devices for separating photogenerated electrons and holes. Crucially, the AA stacking heterostructure stabilizes as an indirect semiconductor with a band gap of 1.42 eV and the AA’ stacking is an indirect semiconductor with band gap of 0.21 eV depending on the polarization direction of In₂Se₃. The electronic structure of AA stacking heterostructure demonstrates pronounced tunability under external biaxial strain. Moderate compressive or tensile strain can systematically modulate the band gap value and more significantly, can potentially alter the band offset strength between the layers. This strain-dependent control directly influences the spatial overlap of wavefunctions and the intensity of the internal field governing carrier separation, offering a powerful post-synthesis knob for property engineering. Moreover, the interfacial coupling in the heterostructure leads to a remarkable enhancement of optical absorption across the visible spectrum compared to the constituent In₂Se₃ and SnSe₂ monolayers. This enhancement is attributed to new optical transition pathways enabled by the hybridized electronic states at the interface. The synergistic combination of a tunable type-II band alignment, strong visible-light response, and efficient inherent charge separation mechanism establishes the In₂Se₃/SnSe₂ heterostructure as a highly promising material system for applications in visible-light photocatalysis and novel optoelectronic devices.

近年来，能够可逆自极化的铁电材料由于其在非易失性存储器和自适应电子器件方面的开创性进展而获得了相当大的兴趣。通过密度泛函理论（DFT）的第一性原理计算，构建了由铁电In2Se3和InSe2组成的垂直堆叠In2Se3/SnSe2范德华（vdW）异质结构。结果表明，两种异质结构呈现ii型能带排列，这有利于光催化装置分离光生电子和空穴。关键是，根据In2Se3的极化方向，AA堆叠异质结构稳定为带隙为1.42 eV的间接半导体，AA堆叠异质结构稳定为带隙为0.21 eV的间接半导体。AA叠层异质结构的电子结构在外部双轴应变下表现出明显的可调性。适度的压缩或拉伸应变可以系统地调节带隙值，更重要的是，可以潜在地改变层之间的带偏移强度。这种依赖于应变的控制直接影响波函数的空间重叠和控制载流子分离的内部场的强度，为性能工程提供了强大的合成后旋钮。此外，与组成In2Se3和SnSe2单层相比，异质结构中的界面耦合导致在可见光谱上的光吸收显著增强。这种增强是由于界面上的杂化电子态实现了新的光学跃迁途径。In2Se3/SnSe2异质结构具有可调谐的ii型波段对准、强大的可见光响应和高效的内在电荷分离机制，这三者的协同结合使其成为一种非常有前途的材料体系，可用于可见光光催化和新型光电器件。

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引用次数: 0

Hydrogen storage thermodynamics on a single-site scandium metalloporphyrin 单点金属卟啉钪的储氢热力学

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

Computational Materials Science

Pub Date : 2026-03-10 Epub Date: 2026-02-13 DOI: 10.1016/j.commatsci.2026.114546

İskender Muz , Mustafa Kurban

Reversible hydrogen storage on single-site platforms requires interactions strong enough to offset the gas-to-adsorbate entropy penalty, yet weak enough to avoid H₂ activation. Here we investigate a scandium metalloporphyrin (Sc-MP) as a model single-site adsorbent for multi-H₂ loading (n = 1–20) using dispersion-corrected DFT (ωB97X-D/def2-TZVP) combined with a van't Hoff thermodynamic analysis. Across all coverages, the optimized geometries show strictly molecular adsorption: HH bond lengths remain near the gas-phase value (0.74–0.76 Å), the proximal Sc···H₂ contact is essentially load-invariant (2.33–2.50 Å), and a distal shell with Sc···H₂ ≥ 5 Å emerges beyond n = 9, evidencing a compact first shell plus a weakly coupled second shell. The average adsorption energy per H₂ decreases from −0.157 eV (n = 1) to −0.012 eV (n = 20), while the adsorption enthalpy |ΔH| weakens from 17.6 to 2.2 kJ mol⁻¹ and ΔG(298 K, 1 bar) remains positive. Entropies extracted from ΔH/ΔG (−95 to −76 J mol⁻¹ K⁻¹) feed a van't Hoff treatment that yields a quantitative T–p map: at 77 K and 1–10 bar, moderate reversible loadings (upper-bound n = 4–6) are thermodynamically favored, whereas at 150 K only n = 1 is stable. Electronic-structure signatures, an essentially constant HOMO–LUMO gap (3.95–3.97 eV), modest charge redistribution on Sc (+1.16 → +0.40 |e|), and PDOS with weak H-s intensity and no σ*(H₂) band, corroborate a polarization-dominated, weak-Kubas regime. Sc-MP thus provides a cluster-resistant single-site scaffold that supports reversible cryogenic storage (up to 10.24 wt% H₂-only) and suggests concrete design levers: modest field tuning to strengthen early adsorption toward the 15–20 kJ mol⁻¹ window and reticulation into porphyrinic frameworks to increase site density while preserving single-site character.

单位点平台上的可逆储氢需要足够强的相互作用来抵消气体到吸附物的熵罚，但又需要足够弱的相互作用来避免H2活化。本文利用色散校正DFT （ωB97X-D/def2-TZVP）结合范霍夫热力学分析，研究了钪金属卟啉（Sc-MP）作为多h2负载（n = 1-20）的模型单位点吸附剂。在所有覆盖范围内，优化后的几何结构表现出严格的分子吸附：HH键长度保持在气相值附近（0.74-0.76 Å），近端Sc··H2接触基本上是负载不变的（2.33-2.50 Å），远端Sc··H2≥5 Å出现在n = 9以上，证明第一壳层紧凑，第二壳层弱耦合。H2的平均吸附能从- 0.157 eV （n = 1）下降到- 0.012 eV (n = 20)，吸附焓|ΔH|从17.6减弱到2.2 kJ mol - 1， ΔG（298 K, 1 bar）保持正值。从ΔH/ΔG提取的熵（- 95至- 76 J mol - 1 K - 1）提供范霍夫处理，产生定量的T-p图：在77 K和1 - 10 bar时，适度可逆负荷（上限n = 4-6）在热力学上有利，而在150 K时只有n = 1是稳定的。电子结构特征，基本恒定的HOMO-LUMO隙（3.95 ~ 3.97 eV）， Sc上适度的电荷再分配（+1.16→+0.40 |e|），弱H-s强度和无σ*(H2)带的PDOS，证实了极化主导的弱kubas体系。因此，Sc-MP提供了一种抗团簇的单位点支架，支持可逆的低温储存（高达10.24 wt%仅限h2），并建议具体的设计手段：适度的现场调整，以加强对15-20 kJ mol - 1窗口的早期吸附，并网状成卟啉框架，以增加位点密度，同时保持单位点特征。

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引用次数: 0

Calculation of the Ti–Mo phase diagram using density functional theory and crystal symmetry 用密度泛函理论和晶体对称计算Ti-Mo相图

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

Computational Materials Science

Pub Date : 2026-03-10 Epub Date: 2026-02-20 DOI: 10.1016/j.commatsci.2026.114594

Yukinari Ikeda, Akio Ishii

We constructed a Ti–Mo phase diagram, including the

α

,

ω

and

β

phases, by calculating the Gibbs free energy of each phase using density functional theory (DFT) and the cluster variation method (CVM). The enthalpy term was obtained from DFT calculations, and the contribution of lattice vibration was considered using the empirical Debye model. The configurational entropy for each phase was calculated using crystal symmetry and group theory in the CVM framework, wherein the decrease in entropy resulting from the orderliness of the atomic arrangement and the differences in the lattice structure of each phase were considered. The estimated configurational entropy was more accurate than that estimated using the conventional ideal solution approximation. The phase transformation temperature in the calculated phase diagram was lower by 10–100 K than that obtained using the ideal solution approximation. This difference originates from the lattice structures of the cubic

β

phase and the hexagonal

α

and

ω

phases. These results indicate that considering the differences in the crystal symmetry of each phase is necessary for a highly accurate calculation of the configurational entropy.

利用密度泛函理论（DFT）和聚类变分法（CVM）计算各相的吉布斯自由能，构建了包含α、ω和β相的Ti-Mo相图。由DFT计算得到焓项，并利用经验Debye模型考虑晶格振动的贡献。在CVM框架下，利用晶体对称性和群论计算了各相的构型熵，其中考虑了原子排列的有序性和各相晶格结构的差异导致的熵的减少。估计的构型熵比传统的理想解近似估计的更精确。计算得到的相图相变温度比采用理想溶液近似法得到的相变温度低10 ~ 100 K。这种差异源于立方β相和六方α、ω相的晶格结构。这些结果表明，考虑各相晶体对称性的差异对于高度精确地计算构型熵是必要的。

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引用次数: 0

Linear-response TDDFT and supercell core-hole calculations of electron energy-loss spectra in polymorphic HfO2 多态HfO2中电子能量损失谱的线性响应TDDFT和超级单体核空穴计算

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

Computational Materials Science

Pub Date : 2026-03-10 Epub Date: 2026-02-25 DOI: 10.1016/j.commatsci.2026.114601

Jiachen Fan, Shang-Peng Gao

Due to its outstanding electronic and dielectric properties, hafnium dioxide (HfO₂) has emerged as a promising material across diverse fields, from high-κ dielectrics to next-generation non-volatile memories and optical devices. A comprehensive understanding of the dielectric response and electronic excitation characteristics of HfO₂ is essential for both fundamental studies and device applications. Electron energy-loss spectroscopy (EELS), a key technique for probing dielectric behavior and electronic structure, plays a crucial role in characterizing polymorphic HfO₂. In this study, the low-loss and core-loss EELS spectra of cubic (c), tetragonal (t), monoclinic (m) and orthorhombic-III (oIII) HfO₂ are systematically investigated using first-principles calculations. In the low-loss region, anisotropic EELS spectra are obtained via time-dependent density functional theory (TDDFT) with random phase approximation (RPA) and adiabatic local density approximation (ALDA), including local-field effects (LFEs), and the influence of finite momentum transfer on the energy and intensity evolution of characteristic excitations is thoroughly examined. In the core-loss region, anisotropic O K-edge energy-loss near-edge structures (ELNES) are calculated using a core-excited pseudopotential approach incorporating core-hole effects. The spectral features are analyzed in conjunction with the projected density of states (PDOS) to elucidate their electronic origins, and the roles of local chemical coordination on the ELNES are further assessed. This work offers rigorous theoretical insight into the electronic excitation properties of polymorphic HfO₂. The findings provide a deeper understanding of electronic excitation behavior and dielectric response in different HfO₂ polymorphs, thereby advancing the interpretation of their EELS spectra and supporting the optimization of HfO₂-based electronic devices.

由于其出色的电子和介电性能，二氧化铪（HfO2）已成为从高κ介电材料到下一代非易失性存储器和光学器件等各个领域的有前途的材料。全面了解HfO2的介电响应和电子激发特性对于基础研究和器件应用都是必不可少的。电子能量损失谱（EELS）是探测HfO2介电行为和电子结构的关键技术，在表征多晶态HfO2中起着至关重要的作用。在本研究中，采用第一性原理计算系统地研究了立方(c)，四方(t)，单斜(m)和正交- iii (oIII) HfO2的低损耗和核心损耗EELS谱。在低损耗区，采用随机相位近似（RPA）和绝热局部密度近似（ALDA），利用随时间密度泛函理论（TDDFT）获得了EELS各向异性谱，包括局域场效应（LFEs），并深入研究了有限动量传递对特征激发能量和强度演化的影响。在核损耗区，利用核激发伪势方法计算了各向异性O - k边能量损失近边结构（ELNES）。结合预测态密度（PDOS）分析了光谱特征，阐明了它们的电子来源，并进一步评估了局部化学配位对ELNES的作用。这项工作为多晶HfO2的电子激发特性提供了严格的理论见解。研究结果对不同HfO2多晶的电子激发行为和介电响应有了更深入的了解，从而促进了对其EELS谱的解释，并为HfO2基电子器件的优化提供了支持。

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引用次数: 0

Improved capabilities of the TurboGAP code for radiation induced cascade simulations: An illustration with silicon 改进的TurboGAP代码的能力，辐射诱导级联模拟：一个插图与硅

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

Computational Materials Science

Pub Date : 2026-03-10 Epub Date: 2026-02-23 DOI: 10.1016/j.commatsci.2026.114560

Uttiyoarnab Saha , Ali Hamedani , Miguel A. Caro , Andrea E. Sand

TurboGAP is a software package designed for efficient molecular dynamics simulations using Gaussian approximation potential (GAP) machine-learning interatomic potentials (MLIP). In this work, we enhance the capabilities of TurboGAP for radiation damage simulations by implementing a two-temperature molecular dynamics model, based on electron density-dependent coupling of electronic and atomic subsystems. Additionally, we implement adaptive calculation of the timestep and grouping of atoms for cell-border cooling. Our implementation incorporates electronic stopping power either through a traditional friction-based model or a more realistic first-principles-derived model. By combining the computational efficiency of TurboGAP with the accuracy of GAP MLIP, we perform cascade simulations in silicon with primary knock-on atom (PKA) energies up to 10 keV. Our simulations scale to systems containing up to 1 million atoms. We study the generation and clustering of radiation-induced defects. We also calculate ion-beam mixing and compare our results with the experimental data, discussing how the GAP-MLIP along with the inclusion of a realistic electronic stopping model affects the prediction of experimental mixing values.

TurboGAP是一个使用高斯近似势（GAP）机器学习原子间势（MLIP）进行有效分子动力学模拟的软件包。在这项工作中，我们通过实现基于电子和原子子系统的电子密度依赖耦合的双温度分子动力学模型，增强了TurboGAP辐射损伤模拟的能力。此外，我们实现了自适应计算的时间步长和分组原子的细胞边界冷却。我们的实现通过传统的基于摩擦的模型或更现实的第一原理衍生模型集成了电子停止功率。通过将TurboGAP的计算效率与GAP MLIP的精度相结合，我们在硅中进行了一级撞击原子（PKA）能量高达10 keV的级联模拟。我们的模拟扩展到包含多达100万个原子的系统。我们研究了辐射缺陷的产生和聚类。我们还计算了离子束混合，并将结果与实验数据进行了比较，讨论了GAP-MLIP以及包含现实电子停止模型如何影响实验混合值的预测。

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引用次数: 0

Numerical efficiency of explicit time integrators for phase-field models 相场模型显式时间积分器的数值效率

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

Computational Materials Science

Pub Date : 2026-03-10 Epub Date: 2026-02-20 DOI: 10.1016/j.commatsci.2026.114599

Marco Seiz , Tomohiro Takaki

Phase-field simulations are a practical but also expensive tool to calculate microstructural evolution. This work aims to compare explicit time integrators for a broad class of phase-field models involving coupling between the phase-field and concentration. Particular integrators are adapted to constraints on the phase-field as well as storage scheme implications. Reproducible benchmarks are defined with a focus on having exact sharp interface solutions, allowing for identification of dominant error terms. Speedups of 4 to 114 over the classic forward Euler integrator are achievable while still using a fully explicit scheme without appreciable accuracy loss. Application examples include final stage sintering with pores slowing down grain growth as they move and merge over time.

相场模拟是一种实用但昂贵的计算微观结构演变的工具。这项工作旨在比较涉及相场和浓度之间耦合的广泛的相场模型的显式时间积分器。特定的积分器适应于相场的约束以及存储方案的含义。定义可重复的基准时，重点是要有精确的接口解决方案，以便识别主要的错误项。4到114的加速超过经典的前向欧拉积分器是可以实现的，同时仍然使用一个完全显式的方案，没有明显的精度损失。应用实例包括烧结的最后阶段，随着时间的推移，孔隙的移动和合并会减缓晶粒的生长。

引用次数: 0

Beyond prediction: Assessing stability in feature selection methods for materials science applications 超越预测：评估材料科学应用中特征选择方法的稳定性

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

Computational Materials Science

Pub Date : 2026-03-10 Epub Date: 2026-02-25 DOI: 10.1016/j.commatsci.2026.114609

Yoshiyasu Takefuji

This study examines the reliability of feature selection methods in materials science, where machine learning applications have surged despite widespread misapplications stemming from limited understanding of interpretability constraints. We compare supervised models (XGBoost, Random Forest), unsupervised techniques (Feature Agglomeration, HVGS), and statistical methods (Spearman's correlation) through a novel stability testing framework using a public materials dataset. Our results reveal that despite high predictive accuracy (R² > 0.95), supervised models produce unstable feature rankings when the highest-ranked feature is removed—a critical flaw when identifying structure-property relationships. Common misapplications include over-reliance on black-box models for scientific interpretation, insufficient cross-validation procedures, and failure to test feature importance stability. In contrast, unsupervised methods and Spearman's correlation demonstrate perfect ranking stability while maintaining competitive performance. This highlights a fundamental distinction between prediction accuracy and feature importance reliability. We recommend that materials researchers supplement supervised learning with model-agnostic approaches to avoid misinterpretation of material-property relationships and ensure scientifically robust conclusions about causal mechanisms in materials development.

本研究考察了材料科学中特征选择方法的可靠性，尽管由于对可解释性约束的理解有限而导致广泛的误用，但机器学习应用仍在激增。我们通过使用公共材料数据集的新型稳定性测试框架，比较了有监督模型（XGBoost, Random Forest）、无监督技术（Feature Agglomeration， HVGS）和统计方法（Spearman’s correlation）。我们的研究结果表明，尽管预测精度很高（R2 > 0.95），但当移除排名最高的特征时，监督模型会产生不稳定的特征排名——这是识别结构-属性关系时的一个关键缺陷。常见的错误应用包括过度依赖黑盒模型进行科学解释，交叉验证过程不足，以及未能测试特征重要性的稳定性。相比之下，无监督方法和斯皮尔曼相关性在保持竞争绩效的同时表现出完美的排名稳定性。这突出了预测准确性和特征重要性可靠性之间的根本区别。我们建议材料研究人员用模型不可知的方法来补充监督学习，以避免对材料-属性关系的误解，并确保对材料开发中的因果机制得出科学可靠的结论。

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引用次数: 0

Expanding the search space of high entropy oxides and predicting synthesizability using machine learning interatomic potentials 扩展高熵氧化物的搜索空间并利用机器学习原子间势预测可合成性

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

Computational Materials Science

Pub Date : 2026-03-10 Epub Date: 2026-02-11 DOI: 10.1016/j.commatsci.2026.114581

Oliver A. Dicks , Solveig S. Aamlid , Alannah M. Hallas , Joerg Rottler

We propose an efficient computational methodology for predicting the synthesizability of high entropy oxides (HEOs) in a large space of possible candidate compounds. HEOs are a growing field with an enormous potential chemical composition space, and yet the discovery of new HEOs is slow and driven by experimental trial-and-error. In this work, we attempt to speed up this process by using a machine learned interatomic potential offering DFT-level accuracy. Our methodology starts by identifying a set of crystal structures and elements for screening, building a large random unit cell of each composition and structure, then relaxing this structure. The most promising candidates are distinguished based on the variance of the individual cation energies, which we introduce as our novel entropy descriptor, and the enthalpy of mixing, which is used as the enthalpy descriptor. The approach is applied to tetravalent HEOs, and its validity is confirmed by comparison to alternative descriptors and DFT calculations for a set of 7 elements. The search is then extended to a set of 14 elements and three crystal structures, where it successfully identifies the only known stable 4-component HEO in the

α

-PbO

_{2}

structure, as well as predicting several new 5-component candidate systems. This approach can straightforwardly be applied to new sets of elements and structures, allowing for the accelerated discovery of new HEOs.

我们提出了一种有效的计算方法来预测高熵氧化物（HEOs）在可能的候选化合物的大空间中的可合成性。heo是一个不断发展的领域，具有巨大的潜在化学成分空间，然而新的heo的发现是缓慢的，并且受到实验试错的驱动。在这项工作中，我们试图通过使用提供dft级别精度的机器学习原子间势来加速这一过程。我们的方法首先确定一组用于筛选的晶体结构和元素，然后构建每个组成和结构的大型随机单元格，然后放松该结构。最有希望的候选者是根据单个阳离子能量的方差来区分的，我们引入了新的熵描述符，以及混合焓，它被用作焓描述符。将该方法应用于四价heo，并通过与7个元素的替代描述符和DFT计算的比较证实了其有效性。然后将搜索扩展到14个元素和3种晶体结构，成功地确定了α-PbO2结构中唯一已知的稳定的4组分HEO，并预测了几个新的5组分候选体系。这种方法可以直接应用于新的元素和结构，从而加速发现新的heo。

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引用次数: 0

A deep learning strategy to calibrate heteroatomic interactions in metal alloys 一种校准金属合金中杂原子相互作用的深度学习策略

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

Computational Materials Science

Pub Date : 2026-03-10 Epub Date: 2026-02-14 DOI: 10.1016/j.commatsci.2026.114586

Luca Benzi , Diana Nelli , Pascal Andreazza , Riccardo Ferrando , Georg Daniel Förster

A machine-learning-assisted strategy is proposed to calibrate the heteronuclear parameters of the Tight-Binding Second-Moment Approximation (TB–SMA) potential using finite-temperature experimental data. The method involves the use of neural-network surrogate models trained on a large dataset of fictitious binary alloys, generated by randomly sampling TB–SMA parameter sets within physically meaningful intervals. Each surrogate model learns to predict thermodynamic observables — mixing enthalpy and lattice parameter — directly from the potential parameters. Once trained, the networks provide instantaneous predictions, eliminating the need for costly simulations during the optimization loop. The surrogate models are then embedded in a minimization scheme that adjusts the mixed interaction parameters to reproduce experimental thermodynamic data at selected compositions and at given temperatures. This workflow is applied to ten binary alloys formed by Cu, Ni, Pt, Pd, and Rh, obtaining parametrizations that accurately match experimental trends. The approach is general and well adapted to complex multi-element systems as high-entropy alloys. It can be extended to other potential forms and target properties.

提出了一种机器学习辅助策略，利用有限温度实验数据校准紧密结合第二矩近似（TB-SMA）势的异核参数。该方法使用神经网络代理模型，该模型是在虚拟二元合金的大型数据集上训练的，该数据集是在物理上有意义的间隔内随机抽样TB-SMA参数集生成的。每个代理模型学习预测热力学观测-混合焓和晶格参数-直接从势参数。一旦训练，网络提供即时预测，消除了优化循环期间昂贵的模拟的需要。然后将代理模型嵌入到最小化方案中，该方案调整混合相互作用参数，以在选定成分和给定温度下再现实验热力学数据。该工作流程应用于由Cu， Ni, Pt， Pd和Rh组成的十种二元合金，获得准确匹配实验趋势的参数化。该方法具有通用性，适用于复杂的多元素系统，如高熵合金。它可以扩展到其他潜在的形式和目标属性。

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引用次数: 0

Sample-efficient active learning for materials informatics using integrated posterior variance 基于后验方差的材料信息学样本有效主动学习

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

Computational Materials Science

Pub Date : 2026-03-10 Epub Date: 2026-02-09 DOI: 10.1016/j.commatsci.2026.114551

Ramsey Issa , Said Hamad , Ricardo Grau-Crespo , Emad Awad , Taylor D. Sparks

Developing accurate machine learning models with minimal data remains a central challenge in materials informatics. Efficient models can significantly reduce costly computational simulations and time-intensive experimentation by providing reliable predictions of material properties. In this work, we investigate the integrated posterior variance acquisition function within an active learning framework, comparing its performance against three established methods: random sampling, point-wise uncertainty sampling, and query-by-committee. We evaluate these methods across three diverse datasets: AutoAM, Thermoelectric, and NMR. Our results demonstrate that integrated posterior variance consistently outperforms conventional methods in selecting candidates that minimize prediction error with fewer labeled samples. We identify two key limitations: computational overhead that increases with dataset size and diminished effectiveness in high-dimensional feature spaces where distance metrics become less meaningful. Despite these constraints, our approach demonstrates how strategic experimental selection can substantially improve model performance across varying materials informatics domains while minimizing the number of required experiments, offering significant resource savings for materials discovery workflows.

用最少的数据开发准确的机器学习模型仍然是材料信息学的核心挑战。有效的模型可以通过提供可靠的材料性能预测，显著减少昂贵的计算模拟和时间密集的实验。在这项工作中，我们研究了主动学习框架内的综合后验方差获取函数，并将其与三种既定方法（随机抽样、点不确定性抽样和按委员会查询）的性能进行了比较。我们在三个不同的数据集上评估这些方法：AutoAM， Thermoelectric和NMR。我们的结果表明，综合后验方差始终优于传统的选择方法，以减少标记样本的预测误差。我们确定了两个关键的限制：计算开销随着数据集大小的增加而增加，并且在距离度量变得没有意义的高维特征空间中有效性降低。尽管存在这些限制，我们的方法证明了策略性实验选择如何在不同材料信息学领域显著提高模型性能，同时最大限度地减少所需实验的数量，为材料发现工作流程节省大量资源。

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引用次数: 0