Pub Date : 2026-03-10Epub Date: 2026-02-17DOI: 10.1016/j.commatsci.2026.114583
Chongfeng Zhang , Yi Song , Leiji Li , Xiaopeng Shen , Weijun Wang , Tianchi Zhu , Fei Xiao
Metal additive manufacturing (AM) offers unprecedented design flexibility and efficiency, yet its performance is critically governed by rapid solidification phenomena. In this paper, we offer an in-depth analysis regarding non-equilibrium effects. Specifically, the discussion centers on critical mechanisms including solute trapping, solute drag as well as interface dynamics, and their role in shaping microstructure evolution during rapid cooling. Specific attention will be given to dendritic, eutectic, peritectic solidification, and banded structures, which are characteristic of metal AM. In parallel, the review highlights the latest advances in multiscale modeling, spanning molecular dynamics, kinetic Monte Carlo, cellular automata, and phase-field approaches. By linking atomistic processes to mesoscopic pattern formation, this article will offer a comprehensive perspective that connects fundamental solidification science with predictive simulation tools. The paper closes by identifying critical obstacles and potential avenues for future research.
{"title":"On rapid solidification and multiscale modeling in metal additive manufacturing: A review","authors":"Chongfeng Zhang , Yi Song , Leiji Li , Xiaopeng Shen , Weijun Wang , Tianchi Zhu , Fei Xiao","doi":"10.1016/j.commatsci.2026.114583","DOIUrl":"10.1016/j.commatsci.2026.114583","url":null,"abstract":"<div><div>Metal additive manufacturing (AM) offers unprecedented design flexibility and efficiency, yet its performance is critically governed by rapid solidification phenomena. In this paper, we offer an in-depth analysis regarding non-equilibrium effects. Specifically, the discussion centers on critical mechanisms including solute trapping, solute drag as well as interface dynamics, and their role in shaping microstructure evolution during rapid cooling. Specific attention will be given to dendritic, eutectic, peritectic solidification, and banded structures, which are characteristic of metal AM. In parallel, the review highlights the latest advances in multiscale modeling, spanning molecular dynamics, kinetic Monte Carlo, cellular automata, and phase-field approaches. By linking atomistic processes to mesoscopic pattern formation, this article will offer a comprehensive perspective that connects fundamental solidification science with predictive simulation tools. The paper closes by identifying critical obstacles and potential avenues for future research.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"267 ","pages":"Article 114583"},"PeriodicalIF":3.3,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147403835","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}
Pub Date : 2026-02-28Epub Date: 2026-02-01DOI: 10.1016/j.commatsci.2026.114555
Fangwei Yang , Haoran Sun , Xiaoxin Yang , Xu Li , Gang Yang
In recent years, the lattice thermal conductivity of γ-Ga2O3 with a defective spinel structure has attracted widespread attention from both industry and academia. However, due to its inherent structural disorder, accurately predicting its thermal conductivity using first-principles methods remains challenging. To overcome this challenge, this study developed a machine-learning interatomic potential applicable to multiple γ-Ga2O3 configurations, based on the neuroevolution potential framework combined with a multi-round active-learning strategy. Using this potential, the thermal conductivity of different γ-Ga2O3 configurations along various crystallographic directions was calculated. The results show that, within the same structure, the thermal conductivity along the [100] and [010] directions is essentially the same, while it is significantly lower along the [001] direction. Furthermore, the thermal conductivity of all configurations originates primarily from low-frequency phonons in the 0–6 THz range. The highly disordered structure intensifies phonon scattering and significantly reduces the group velocity, resulting in limited actual contribution of high-frequency phonons to thermal transport. Additionally, different configurations exhibit high similarity in phonon transport characteristics, resulting in relatively small differences in thermal conductivity among them.
{"title":"A neuroevolution potential for predicting the lattice thermal conductivity of structurally disordered γ-Ga2O3","authors":"Fangwei Yang , Haoran Sun , Xiaoxin Yang , Xu Li , Gang Yang","doi":"10.1016/j.commatsci.2026.114555","DOIUrl":"10.1016/j.commatsci.2026.114555","url":null,"abstract":"<div><div>In recent years, the lattice thermal conductivity of γ-Ga<sub>2</sub>O<sub>3</sub> with a defective spinel structure has attracted widespread attention from both industry and academia. However, due to its inherent structural disorder, accurately predicting its thermal conductivity using first-principles methods remains challenging. To overcome this challenge, this study developed a machine-learning interatomic potential applicable to multiple γ-Ga<sub>2</sub>O<sub>3</sub> configurations, based on the neuroevolution potential framework combined with a multi-round active-learning strategy. Using this potential, the thermal conductivity of different γ-Ga<sub>2</sub>O<sub>3</sub> configurations along various crystallographic directions was calculated. The results show that, within the same structure, the thermal conductivity along the [100] and [010] directions is essentially the same, while it is significantly lower along the [001] direction. Furthermore, the thermal conductivity of all configurations originates primarily from low-frequency phonons in the 0–6 THz range. The highly disordered structure intensifies phonon scattering and significantly reduces the group velocity, resulting in limited actual contribution of high-frequency phonons to thermal transport. Additionally, different configurations exhibit high similarity in phonon transport characteristics, resulting in relatively small differences in thermal conductivity among them.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"266 ","pages":"Article 114555"},"PeriodicalIF":3.3,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186284","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}
Pub Date : 2026-02-28Epub Date: 2026-02-07DOI: 10.1016/j.commatsci.2026.114577
Stepan Savka, Andriy Serednytski, Dmytro Popovych
β-Gallium oxide (β-Ga₂O₃) is a promising wide-bandgap semiconductor for power electronics, requiring accurate molecular dynamics (MD) simulations to understand its atomic-scale behavior. This work presents the first automated optimization of ReaxFF parameters for β-Ga₂O₃ using Gaussian Process (GP) Bayesian optimization with a multi-objective framework incorporating pressure matching, force matching, and NVE stability testing. We optimized 22 critical ReaxFF parameters including bond energies, bond lengths, angle parameters, van der Waals interactions, and electronic properties. Reference data were obtained from MACE-MP-0, a universal machine learning potential trained on >150,000 DFT calculations. The multi-objective optimization achieved validated NVE ensemble stability at 0.1 fs timestep, with equilibrium pressure matching within 1.2% of MACE-MP-0 predictions (6.75 vs 6.67 GPa). The optimized parameters accurately reproduce experimental structural properties (lattice parameters within 0.3–2.6%, GaO bonds within 1%) and elastic constants within 2% of DFT values. Systematic timestep testing at 0.1, 0.25, and 0.5 fs confirmed that 0.1 fs is optimal for stable dynamics, characteristic of ReaxFF potentials with stiff bond terms. Parameter importance analysis revealed that van der Waals interactions and bond energies are most critical for accurate Ga₂O₃ modeling. The GP-Bayesian framework with multi-objective optimization successfully produced production-ready ReaxFF parameters for β-Ga₂O₃ MD simulations, demonstrating an efficient approach for developing reactive force fields with validated dynamic stability.
β-氧化镓（β-Ga₂O₃）是一种很有前途的用于电力电子的宽带隙半导体，需要精确的分子动力学（MD）模拟来理解其原子尺度的行为。这项工作首次使用高斯过程（GP）贝叶斯优化对β-Ga₂O₃的ReaxFF参数进行了自动优化，该优化具有多目标框架，包括压力匹配、力匹配和NVE稳定性测试。我们优化了ReaxFF的22个关键参数，包括键能、键长、角参数、范德华相互作用和电子性质。参考数据来自MACE-MP-0， MACE-MP-0是一种通用机器学习潜力，经过150,000次DFT计算训练。多目标优化在0.1 fs时间步长下实现了有效的NVE集成稳定性，平衡压力匹配在MACE-MP-0预测的1.2%以内（6.75 vs 6.67 GPa）。优化后的参数准确再现了实验结构性能（晶格参数在0.3-2.6%之间，GaO键在1%之间）和弹性常数在DFT值的2%以内。在0.1、0.25和0.5 fs的系统时间步长测试证实，0.1 fs是稳定动力学的最佳选择，具有刚性键项的ReaxFF电位的特征。参数重要性分析表明，范德华相互作用和键能对于精确的Ga₂O₃建模是最关键的。基于多目标优化的GP-Bayesian框架成功地为β-Ga₂O₃MD模拟生成了生产就绪的ReaxFF参数，展示了一种有效的方法来开发具有动态稳定性的反作用力场。
{"title":"ReaxFF parameter optimization for β-Ga₂O₃ MD simulations using Gaussian process Bayesian optimization","authors":"Stepan Savka, Andriy Serednytski, Dmytro Popovych","doi":"10.1016/j.commatsci.2026.114577","DOIUrl":"10.1016/j.commatsci.2026.114577","url":null,"abstract":"<div><div>β-Gallium oxide (β-Ga₂O₃) is a promising wide-bandgap semiconductor for power electronics, requiring accurate molecular dynamics (MD) simulations to understand its atomic-scale behavior. This work presents the first automated optimization of ReaxFF parameters for β-Ga₂O₃ using Gaussian Process (GP) Bayesian optimization with a multi-objective framework incorporating pressure matching, force matching, and NVE stability testing. We optimized 22 critical ReaxFF parameters including bond energies, bond lengths, angle parameters, van der Waals interactions, and electronic properties. Reference data were obtained from MACE-MP-0, a universal machine learning potential trained on >150,000 DFT calculations. The multi-objective optimization achieved validated NVE ensemble stability at 0.1 fs timestep, with equilibrium pressure matching within 1.2% of MACE-MP-0 predictions (6.75 vs 6.67 GPa). The optimized parameters accurately reproduce experimental structural properties (lattice parameters within 0.3–2.6%, Ga<img>O bonds within 1%) and elastic constants within 2% of DFT values. Systematic timestep testing at 0.1, 0.25, and 0.5 fs confirmed that 0.1 fs is optimal for stable dynamics, characteristic of ReaxFF potentials with stiff bond terms. Parameter importance analysis revealed that van der Waals interactions and bond energies are most critical for accurate Ga₂O₃ modeling. The GP-Bayesian framework with multi-objective optimization successfully produced production-ready ReaxFF parameters for β-Ga₂O₃ MD simulations, demonstrating an efficient approach for developing reactive force fields with validated dynamic stability.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"266 ","pages":"Article 114577"},"PeriodicalIF":3.3,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186227","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}
Pub Date : 2026-02-28Epub Date: 2026-02-01DOI: 10.1016/j.commatsci.2026.114517
Edward Kim , Jason Hattrick-Simpers
{"title":"Perspective: Multi-shot LLMs are useful for literature summaries, but humans should remain in the loop","authors":"Edward Kim , Jason Hattrick-Simpers","doi":"10.1016/j.commatsci.2026.114517","DOIUrl":"10.1016/j.commatsci.2026.114517","url":null,"abstract":"","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"266 ","pages":"Article 114517"},"PeriodicalIF":3.3,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186285","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}
Pub Date : 2026-02-28Epub Date: 2026-02-06DOI: 10.1016/j.commatsci.2026.114562
Qin Qin , Yawen Hua , Luyao Hai , Meidie Wu , Siqi Jiang , Rongxing Ye , Jiangfeng Song , Yiliang Liu , Linsen Zhou
Zirconium‑cobalt (ZrCo) alloy is a promising candidate for replacing uranium in tritium storage, yet its practical application is limited by disproportionation-induced capacity decay. This study explores the effect of multi-V doping on the configurations, mechanical properties, and hydrogen storage behavior of ZrCo alloys. Specifically, V dopants induce lattice contraction owing to their smaller atomic radius and exhibit an energetically preferred homogeneous dopant dispersion. Mechanistically, the substitutional strengthening effect is highly sensitive to the concentration and configuration of V dopants, with an optimal concentration of ∼11.1%. Furthermore, multiple-V doping can enhance the thermodynamic stability of hydrogen at OCT1 interstitial sites and lower the migration barrier for hydrogen diffusion, thereby facilitating hydriding/dehydriding kinetics in ZrCo alloys. For β-phase hydrides, it significantly improves the anti-disproportionation performance through a synergistic mechanism involving structural reduction of 8e site volume, thermodynamic destabilization of H(8e) occupation, and kinetic facilitation of H(8e) egress. These findings provide a theoretical basis for designing high-performance ZrCoalloys for advanced tritium storage applications.
{"title":"Mechanisms of multiple V-doping in tuning mechanical and hydrogen storage properties of ZrCo alloys","authors":"Qin Qin , Yawen Hua , Luyao Hai , Meidie Wu , Siqi Jiang , Rongxing Ye , Jiangfeng Song , Yiliang Liu , Linsen Zhou","doi":"10.1016/j.commatsci.2026.114562","DOIUrl":"10.1016/j.commatsci.2026.114562","url":null,"abstract":"<div><div>Zirconium‑cobalt (ZrCo) alloy is a promising candidate for replacing uranium in tritium storage, yet its practical application is limited by disproportionation-induced capacity decay. This study explores the effect of multi-V doping on the configurations, mechanical properties, and hydrogen storage behavior of ZrCo alloys. Specifically, V dopants induce lattice contraction owing to their smaller atomic radius and exhibit an energetically preferred homogeneous dopant dispersion. Mechanistically, the substitutional strengthening effect is highly sensitive to the concentration and configuration of V dopants, with an optimal concentration of ∼11.1%. Furthermore, multiple-V doping can enhance the thermodynamic stability of hydrogen at OCT1 interstitial sites and lower the migration barrier for hydrogen diffusion, thereby facilitating hydriding/dehydriding kinetics in ZrCo alloys. For <em>β</em>-phase hydrides, it significantly improves the anti-disproportionation performance through a synergistic mechanism involving structural reduction of 8e site volume, thermodynamic destabilization of H(8e) occupation, and kinetic facilitation of H(8e) egress. These findings provide a theoretical basis for designing high-performance ZrCoalloys for advanced tritium storage applications.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"266 ","pages":"Article 114562"},"PeriodicalIF":3.3,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186231","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}
Pub Date : 2026-02-28Epub Date: 2026-02-06DOI: 10.1016/j.commatsci.2026.114566
Yang Li , Zhihui Wang , Wei Zhou , Rui Wang , Haiyan Zhang , Shu Zhan , Jiajia Xu
Driven by the rapid progress of high-throughput DFT calculations and the expansion of materials databases, machine learning has become increasingly central to the prediction of materials properties. Traditional descriptor-driven models, though physically interpretable, often fail to comprehensively capture the high-order geometric characteristics of complex crystals. To address this limitation, this study proposes FBformer, a crystal property prediction model based on periodic graph encoding. Built upon the Matformer framework, FBformer introduces four-body features, including bond angles and dihedral angles, to explicitly model crystal periodicity and multi-body interactions. By constructing a dual-graph architecture that integrates atomic and angular representations, FBformer effectively fuses atomic types, bond lengths, bond angles, and dihedral angles across multi-level node and edge embeddings, thereby enhancing the model's structural representation capability. Across the eight prediction tasks on the Materials Project and JARVIS-DFT databases, except for formation energy on the Materials Project, FBformer significantly outperforms existing models in predicting Ehull, formation energy on JARVIS-DFT, bandgap, total energy, bulk moduli, and shear moduli. Ablation experiments show that progressively incorporating three-body and four-body features consistently enhances model performance, underscoring the crucial importance of high-order geometric information in crystal property modeling. This study presents novel conceptual and methodological contributions that drive the deeper convergence of AI and materials science, and lays a solid foundation for the efficient prediction and design of novel crystalline materials. The source code can be accessed at: https://github.com/YangLi2025/FBformer.
{"title":"FBformer: A four-body feature enhanced periodic graph transformer for crystal property prediction","authors":"Yang Li , Zhihui Wang , Wei Zhou , Rui Wang , Haiyan Zhang , Shu Zhan , Jiajia Xu","doi":"10.1016/j.commatsci.2026.114566","DOIUrl":"10.1016/j.commatsci.2026.114566","url":null,"abstract":"<div><div>Driven by the rapid progress of high-throughput DFT calculations and the expansion of materials databases, machine learning has become increasingly central to the prediction of materials properties. Traditional descriptor-driven models, though physically interpretable, often fail to comprehensively capture the high-order geometric characteristics of complex crystals. To address this limitation, this study proposes FBformer, a crystal property prediction model based on periodic graph encoding. Built upon the Matformer framework, FBformer introduces four-body features, including bond angles and dihedral angles, to explicitly model crystal periodicity and multi-body interactions. By constructing a dual-graph architecture that integrates atomic and angular representations, FBformer effectively fuses atomic types, bond lengths, bond angles, and dihedral angles across multi-level node and edge embeddings, thereby enhancing the model's structural representation capability. Across the eight prediction tasks on the Materials Project and JARVIS-DFT databases, except for formation energy on the Materials Project, FBformer significantly outperforms existing models in predicting Ehull, formation energy on JARVIS-DFT, bandgap, total energy, bulk moduli, and shear moduli. Ablation experiments show that progressively incorporating three-body and four-body features consistently enhances model performance, underscoring the crucial importance of high-order geometric information in crystal property modeling. This study presents novel conceptual and methodological contributions that drive the deeper convergence of AI and materials science, and lays a solid foundation for the efficient prediction and design of novel crystalline materials. The source code can be accessed at: <span><span>https://github.com/YangLi2025/FBformer</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"266 ","pages":"Article 114566"},"PeriodicalIF":3.3,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186233","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}
Pub Date : 2026-02-28Epub Date: 2026-02-07DOI: 10.1016/j.commatsci.2026.114568
Quentin Bizot , Ryo Tamura , Guillaume Deffrennes
The enthalpy of mixing in the liquid phase is an important property for predicting phase formation in alloys. In multicomponent metallic liquids, it can be estimated from the binary interactions using a geometrical model, but data are available in less than a third of the binary systems. The prediction of this property in binary liquids is therefore important, and machine learning has recently achieved the highest accuracy. Further improvements requires acquiring high-quality data in liquids where models are poorly constrained. In this study, we propose an active learning approach to identify in which liquids additional data are most needed to improve an initial dataset that covers over 400 binary liquids. We identify a critical need for new data on liquids containing refractory elements, which we address by performing ab initio molecular dynamics simulations for 29 equimolar alloys of Ir, Os, Re and W. This enables more accurate predictions of the enthalpy of mixing, and we discuss the trends obtained for refractory elements of period 6. We use clustering analysis to interpret the results of active learning and to explore how our features can be linked to Miedema’s semi-empirical theory.
{"title":"Active learning for predicting the enthalpy of mixing in binary liquids based on ab initio molecular dynamics","authors":"Quentin Bizot , Ryo Tamura , Guillaume Deffrennes","doi":"10.1016/j.commatsci.2026.114568","DOIUrl":"10.1016/j.commatsci.2026.114568","url":null,"abstract":"<div><div>The enthalpy of mixing in the liquid phase is an important property for predicting phase formation in alloys. In multicomponent metallic liquids, it can be estimated from the binary interactions using a geometrical model, but data are available in less than a third of the binary systems. The prediction of this property in binary liquids is therefore important, and machine learning has recently achieved the highest accuracy. Further improvements requires acquiring high-quality data in liquids where models are poorly constrained. In this study, we propose an active learning approach to identify in which liquids additional data are most needed to improve an initial dataset that covers over 400 binary liquids. We identify a critical need for new data on liquids containing refractory elements, which we address by performing ab initio molecular dynamics simulations for 29 equimolar alloys of Ir, Os, Re and W. This enables more accurate predictions of the enthalpy of mixing, and we discuss the trends obtained for refractory elements of period 6. We use clustering analysis to interpret the results of active learning and to explore how our features can be linked to Miedema’s semi-empirical theory.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"266 ","pages":"Article 114568"},"PeriodicalIF":3.3,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186228","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}
Pub Date : 2026-02-28Epub Date: 2026-02-01DOI: 10.1016/j.commatsci.2026.114547
Xiangyu Huo , Shuangli Yue , Xian Wang , Donghui Xu , Li Zhang , Mingli Yang
The long-term stability of blue-emitting quantum dots (QDs) remains a challenge for their use in electroluminescent applications. While surface defects are common in colloidal QDs because of their long-chain ligands, electron accumulation is one of the key features during device operation. In this contribution, we investigate the early-stage response of CdSe QDs to accumulated electrons, with a particular focus on the role of surface defects and their evolution upon electron injection. First-principles calculations and ab initio molecular dynamics simulations reveal that the injected electrons preferentially localize at under-coordinated Cd atoms rather than distributing uniformly across the QD, making these defect-associated surface metal atoms partially or fully reduced depending on the number of injected electrons. This leads to a surface reconstruction and consequently to remarkable changes in the electronic and optical properties. Moreover, the electron localization tends to occur at these specific defective sites. The formation energy variations of defects and the formation of in-gap states are found to be responsible for the localization of injected electrons. These findings provide fundamental insights into charge-induced surface processes in CdSe QDs, and highlight the role of surface defects in mediating electron localization and structural rearrangements. They provide a mechanistic basis for future studies on improving the stability of blue-emitting QDs.
{"title":"Atomic-scale response of surface-defective CdSe quantum dot to electron injection","authors":"Xiangyu Huo , Shuangli Yue , Xian Wang , Donghui Xu , Li Zhang , Mingli Yang","doi":"10.1016/j.commatsci.2026.114547","DOIUrl":"10.1016/j.commatsci.2026.114547","url":null,"abstract":"<div><div>The long-term stability of blue-emitting quantum dots (QDs) remains a challenge for their use in electroluminescent applications. While surface defects are common in colloidal QDs because of their long-chain ligands, electron accumulation is one of the key features during device operation. In this contribution, we investigate the early-stage response of CdSe QDs to accumulated electrons, with a particular focus on the role of surface defects and their evolution upon electron injection. First-principles calculations and ab initio molecular dynamics simulations reveal that the injected electrons preferentially localize at under-coordinated Cd atoms rather than distributing uniformly across the QD, making these defect-associated surface metal atoms partially or fully reduced depending on the number of injected electrons. This leads to a surface reconstruction and consequently to remarkable changes in the electronic and optical properties. Moreover, the electron localization tends to occur at these specific defective sites. The formation energy variations of defects and the formation of in-gap states are found to be responsible for the localization of injected electrons. These findings provide fundamental insights into charge-induced surface processes in CdSe QDs, and highlight the role of surface defects in mediating electron localization and structural rearrangements. They provide a mechanistic basis for future studies on improving the stability of blue-emitting QDs.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"266 ","pages":"Article 114547"},"PeriodicalIF":3.3,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186287","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}
Pub Date : 2026-02-28Epub Date: 2026-02-03DOI: 10.1016/j.commatsci.2026.114569
Pedro P.P.O. Borges, Robert O. Ritchie, Mark Asta
Transformation- and twinning-induced plasticity (TRIP and TWIP) have been reported to contribute to the low-temperature deformation of some body-centered cubic (bcc) multi-principal element alloys (MPEAs) containing large fractions of group IV transition metals. The influence of interstitial solutes on the mechanisms underlying these forms of plasticity, however, remains unclear. Using first-principles calculations, we study the effects of interstitial O atoms on the relative stability of bcc and phases and on unstable and twin boundary stacking fault energy profiles in a representative bcc MPEA with high group-IV elemental fraction: NbTaTiHf. We find that O additions generally promote the relaxation of configurations back to their parent bcc structure, therefore inhibiting transformation. Calculations of the Rice parameter for bulk bcc and phases, as well as bcc- interfaces, further show that formation is a potent embrittlement factor, an effect that is enhanced by O additions, suggesting that the formation of bcc- interfaces is energetically preferred over the formation of the bulk phase. By contrast, the Rice parameter for twin boundaries indicates that these interfaces do not embrittle the material, even with O atoms at twin boundaries, providing a more favorable pathway for plastic deformation compared to transformation.
{"title":"Effects of interstitial oxygen on ω transformations and twin formation in bcc NbTaTiHf multi-principal element alloy from first-principles","authors":"Pedro P.P.O. Borges, Robert O. Ritchie, Mark Asta","doi":"10.1016/j.commatsci.2026.114569","DOIUrl":"10.1016/j.commatsci.2026.114569","url":null,"abstract":"<div><div>Transformation- and twinning-induced plasticity (TRIP and TWIP) have been reported to contribute to the low-temperature deformation of some body-centered cubic (bcc) multi-principal element alloys (MPEAs) containing large fractions of group IV transition metals. The influence of interstitial solutes on the mechanisms underlying these forms of plasticity, however, remains unclear. Using first-principles calculations, we study the effects of interstitial O atoms on the relative stability of bcc and <span><math><mi>ω</mi></math></span> phases and on unstable and twin boundary stacking fault energy profiles in a representative bcc MPEA with high group-IV elemental fraction: NbTaTiHf. We find that O additions generally promote the relaxation of <span><math><mi>ω</mi></math></span> configurations back to their parent bcc structure, therefore inhibiting <span><math><mi>ω</mi></math></span> transformation. Calculations of the Rice parameter for bulk bcc and <span><math><mi>ω</mi></math></span> phases, as well as bcc-<span><math><mi>ω</mi></math></span> interfaces, further show that <span><math><mi>ω</mi></math></span> formation is a potent embrittlement factor, an effect that is enhanced by O additions, suggesting that the formation of bcc-<span><math><mi>ω</mi></math></span> interfaces is energetically preferred over the formation of the bulk <span><math><mi>ω</mi></math></span> phase. By contrast, the Rice parameter for twin boundaries indicates that these interfaces do not embrittle the material, even with O atoms at twin boundaries, providing a more favorable pathway for plastic deformation compared to <span><math><mi>ω</mi></math></span> transformation.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"266 ","pages":"Article 114569"},"PeriodicalIF":3.3,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186288","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}
Pub Date : 2026-02-28Epub Date: 2026-01-30DOI: 10.1016/j.commatsci.2026.114545
Jean-Michel Bergheau , Jean-Baptiste Leblond
Intermetallic layers inevitably formed during Fe/Al welding are well known to have a strongly detrimental effect upon the mechanical properties of the welded joint. It is therefore important to reliably predict their thicknesses, so as to be able to minimize them and thus optimize the process. Up to now, the study of the thicknesses of intermetallic layers has almost exclusively relied on purely experimental approaches. These approaches involved growth of the layers under isothermal conditions, and heuristic fitting of the measured thicknesses as square-root-type functions of time — thus implicitly assuming growth to be diffusion-governed. In contrast, the approach proposed here relies on a combination of analytical and numerical tools. The bases of the model proposed are three-fold: (i) the hypothesis of a purely 1D geometry and process; (ii) the equilibrium phase diagram of the Fe/Al system, used in conjunction with the hypothesis of local thermodynamic equilibrium; (iii) different diffusion equations in the various phases. Combination of these elements yields a strongly nonlinear diffusion equation, where the diffusion coefficient depends in a discontinuous way upon the unknown — in practice the local fraction of Fe. An analytical solution is derived in the special case of two phases only and a constant temperature. A step-by-step numerical procedure of solution is also proposed for the general case. This procedure is used to actually calculate the thicknesses of and layers resulting from simple temperature cycles, typical of those encountered in resistance spot welding. The results emphasize the importance of the maximum temperature reached during the thermal cycle, as a governing parameter of these thicknesses.
{"title":"Analytical versus numerical methods of prediction of the thickness of intermetallic layers in Fe/Al welding","authors":"Jean-Michel Bergheau , Jean-Baptiste Leblond","doi":"10.1016/j.commatsci.2026.114545","DOIUrl":"10.1016/j.commatsci.2026.114545","url":null,"abstract":"<div><div>Intermetallic layers inevitably formed during Fe/Al welding are well known to have a strongly detrimental effect upon the mechanical properties of the welded joint. It is therefore important to reliably predict their thicknesses, so as to be able to minimize them and thus optimize the process. Up to now, the study of the thicknesses of intermetallic layers has almost exclusively relied on purely experimental approaches. These approaches involved growth of the layers under isothermal conditions, and heuristic fitting of the measured thicknesses as square-root-type functions of time — thus implicitly assuming growth to be diffusion-governed. In contrast, the approach proposed here relies on a combination of analytical and numerical tools. The bases of the model proposed are three-fold: (i) the hypothesis of a purely 1D geometry and process; (ii) the equilibrium phase diagram of the Fe/Al system, used in conjunction with the hypothesis of local thermodynamic equilibrium; (iii) different diffusion equations in the various phases. Combination of these elements yields a strongly nonlinear diffusion equation, where the diffusion coefficient depends in a discontinuous way upon the unknown — in practice the local fraction of Fe. An analytical solution is derived in the special case of two phases only and a constant temperature. A step-by-step numerical procedure of solution is also proposed for the general case. This procedure is used to actually calculate the thicknesses of <span><math><msub><mrow><mi>FeAl</mi></mrow><mrow><mn>3</mn></mrow></msub></math></span> and <span><math><mrow><msub><mrow><mi>Fe</mi></mrow><mrow><mn>2</mn></mrow></msub><msub><mrow><mi>Al</mi></mrow><mrow><mn>5</mn></mrow></msub></mrow></math></span> layers resulting from simple temperature cycles, typical of those encountered in resistance spot welding. The results emphasize the importance of the maximum temperature reached during the thermal cycle, as a governing parameter of these thicknesses.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"266 ","pages":"Article 114545"},"PeriodicalIF":3.3,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077214","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}
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