npj Computational Materials最新文献_第5页

Large tunneling magnetoresistance in room temperature magnetic tunnel junctions based on altermagnetic XV2Y2O 交磁XV2Y2O室温磁隧道结的大隧穿磁阻

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-16 DOI: 10.1038/s41524-026-02321-5

Guohui Zhan, Yonglong Ga, Tongshuai Zhu, Kun Luo, Jiangtao Liu, Kai Chang, Zhenhua Wu

{"title":"Large tunneling magnetoresistance in room temperature magnetic tunnel junctions based on altermagnetic XV2Y2O","authors":"Guohui Zhan, Yonglong Ga, Tongshuai Zhu, Kun Luo, Jiangtao Liu, Kai Chang, Zhenhua Wu","doi":"10.1038/s41524-026-02321-5","DOIUrl":"https://doi.org/10.1038/s41524-026-02321-5","url":null,"abstract":"This work presents a comprehensive investigation of spin-dependent quantum transport properties of the emerging layered room temperature metallic altermagnet family XV2Y2O (X = K, Rb, Cs; Y = S, Se, Te), based on first-principles calculations combined with the non-equilibrium Green’s function formalism. Two magnetic tunnel junctions (MTJs) are designed: one with altermagnetic XV2Y2O electrodes and another with non-magnetic metallic electrodes. For the former, we examine both conventional MgO barriers and a newly proposed class of lattice-matched insulating barriers X2Y. We demonstrate that conventional MgO barriers suffer from quantum-well resonances in the thin-barrier regime due to anomalous interface reflections. MTJs with X2Y barriers exhibit a generally increasing tunneling magnetoresistance (TMR) trend with barrier thickness, governed by the distinct decay rates of the parallel and antiparallel configurations. Additionally, interfacial engineering could modulate device performance. In the metal-electrode MTJs, the transmission coefficient is larger than in the altermagnetic-electrode case, yielding significantly larger current while still maintaining a high TMR ratio exceeding 106%. Our results establish XV2Y2O as a versatile materials platform for high-performance MTJs, providing clear design principles for the development of room temperature altermagnet-based spintronic devices.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"15 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149010505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Prediction of vacancy formation energies in Ni-based superalloys by density functional theory calculations and machine learning 用密度泛函理论计算和机器学习预测镍基高温合金中空位形成能

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-15 DOI: 10.1038/s41524-026-02284-7

Aditya Sundar, Saro San, Michael C. Gao

{"title":"Prediction of vacancy formation energies in Ni-based superalloys by density functional theory calculations and machine learning","authors":"Aditya Sundar, Saro San, Michael C. Gao","doi":"10.1038/s41524-026-02284-7","DOIUrl":"https://doi.org/10.1038/s41524-026-02284-7","url":null,"abstract":"Thermal vacancies play a critical role in high-temperature Ni-based superalloys, influencing elastic constants, creep resistance, oxidation resistance, etc. Local chemical variations in multicomponent alloys generate a broad distribution of vacancy formation energies, producing low-energy states that increase vacancy concentrations. This study investigates the impact of transition (Cr/Co/Fe), refractory (Nb/Ta/Mo/W) and other alloying elements (Al/Cu/Ti/Mn) on vacancy thermodynamics in 79 FCC Ni-based alloys containing 2–6 elements. Density functional theory-based studies show that Cr/Nb/Ta/Al/Ti introduce significant lattice distortions, partially donate electrons which reduces their self-consistent chemical potentials, and broaden vacancy formation energy distributions (standard deviation up to 0.15 eV). In contrast, Co/Fe/Mo/W show lower charge localization. At typical operational temperatures of 1000 K, calculated vacancy concentrations in Ni96-X12 vary as: Nb > Ti > Ta > Al > Cu > Cr > Fe > Co ~ Ni > Mn ~ Mo > W. Multielement alloys show similar trends, where Cr/Nb/Ta-rich compositions have low-energy states (~0.5 eV) and higher vacancy concentrations. Finally, graph neural networks screened ~5500 virtual compositions, identifying eleven compositions with mean vacancy formation energy >1.75 eV and ~100 times lower vacancy concentration than pure Ni at 1000 K. These results provide valuable guidelines for defect engineering in high-temperature alloys.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"2017 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149010508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

A multimodal semantic learning framework for data-driven discovery of nitrate-to-ammonia electrocatalysts 数据驱动发现硝酸盐制氨电催化剂的多模态语义学习框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-15 DOI: 10.1038/s41524-026-02327-z

Jiajia Liu, Yuying Jiang, Ran Ma, Zhenhui Ma, Shuren Wang, Yanglong Hou

{"title":"A multimodal semantic learning framework for data-driven discovery of nitrate-to-ammonia electrocatalysts","authors":"Jiajia Liu, Yuying Jiang, Ran Ma, Zhenhui Ma, Shuren Wang, Yanglong Hou","doi":"10.1038/s41524-026-02327-z","DOIUrl":"https://doi.org/10.1038/s41524-026-02327-z","url":null,"abstract":"Data-centric approaches are increasingly driving the discovery of functional materials; however, the heterogeneity of experimental conditions and the fragmented nature of data, especially when embedded in unstructured scientific literature, hinder the development of generalizable predictive models. Here, we present a multimodal artificial intelligence (AI) framework, the Material Semantics Network (MatSemNet), which learns materials semantics by integrating textual, numerical, and reaction pathway information extracted from the literature. By constructing a unified representation of structure-property relationships, MatSemNet enables transferable prediction of catalytic performance across material systems, outperforming conventional single-modal or descriptor-based approaches. As a proof of concept, we apply MatSemNet to the nitrate reduction reaction (NO3RR), achieving high predictive accuracy for Faradaic efficiency (FE) and identifying promising catalyst candidates. Guided by the model, we design and experimentally validate rare-earth (RE)-doped Co3O4 catalysts, which deliver an FE of 85.5% and an ammonia yield rate of 28.3 mg h−1 cm−2 under alkaline conditions. Mechanistic studies show that RE incorporation modulates the electronic structure and enhances reaction selectivity. Beyond this specific application, MatSemNet offers a scalable and transferable paradigm for data-driven materials discovery by bridging heterogeneous knowledge with predictive modeling, and it is broadly applicable to diverse catalytic and functional material systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"17 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149010507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Large language model-enabled automated data extraction for concrete materials informatics 基于大语言模型的混凝土材料信息学自动数据提取

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-14 DOI: 10.1038/s41524-026-02304-6

Zhanzhao Li, Kengran Yang, Qiyao He, Kai Gong

{"title":"Large language model-enabled automated data extraction for concrete materials informatics","authors":"Zhanzhao Li, Kengran Yang, Qiyao He, Kai Gong","doi":"10.1038/s41524-026-02304-6","DOIUrl":"https://doi.org/10.1038/s41524-026-02304-6","url":null,"abstract":"The promise of data-driven materials discovery remains constrained by the scarcity of large, high-quality, and accessible experimental datasets. Here, we introduce a generalizable large language model (LLM)-powered pipeline for automated extraction and structuring of materials data from unstructured scientific literature, using concrete materials as a representative and particularly challenging example. The pipeline exhibits robust performance across a broad range of LLMs and achieves an F1 score of up to 0.98 for diverse composition–process–property attributes. Within one hour, it extracts nearly 9000 high-quality records with over 100 attributes from a corpus screened from more than 27,000 publications, enabling the construction of the largest open laboratory database for blended cement concrete. Machine learning analyses underscore the importance of large, diverse, and information-rich datasets for enhancing both in-distribution accuracy and out-of-distribution generalization to unseen materials. The proposed pipeline is readily adaptable to other materials domains and accelerates the development of scalable data infrastructures for materials informatics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"19 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149010509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

AI-accelerated discovery of altermagnetic metal-organic frameworks via interpretable machine learning 人工智能通过可解释的机器学习加速发现电磁金属有机框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-14 DOI: 10.1038/s41524-026-02323-3

Yue Ran, Jingyi Duan, Yikai Chen, Wei Jiang, Yugui Yao

{"title":"AI-accelerated discovery of altermagnetic metal-organic frameworks via interpretable machine learning","authors":"Yue Ran, Jingyi Duan, Yikai Chen, Wei Jiang, Yugui Yao","doi":"10.1038/s41524-026-02323-3","DOIUrl":"https://doi.org/10.1038/s41524-026-02323-3","url":null,"abstract":"Altermagnetism has emerged as a third class of collinear magnetism, yet its exploration remains largely confined to inorganic materials. Here, we establish an AI-accelerated pipeline for discovering altermagnetic metal-organic frameworks (MOFs). From symmetry-guided DFT screening of 350 candidates, we identified six altermagnetic MOFs. A two-stage machine learning strategy was then employed: First, an XGBoost classifier pre-screened 65,578 candidates, narrowing the pool to 145 high-potential linkers, from which DFT confirmed six new altermagnets. Building on this enriched dataset, we trained a KNN classifier (F1 = 0.8793) and a CatBoost regressor (R² = 0.7732) for predicting altermagnetism and spin-splitting magnitudes, respectively. SHAP analysis translated predictions into design principles: conjugated backbones favor altermagnetism, while moderate push-pull groups enhance spin splitting. Guided by these principles, we designed three additional MOF candidates, all confirmed by DFT. In total, this workflow identified 15 altermagnetic MOFs, establishing a generalizable framework for accelerating quantum materials discovery.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"24 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149010510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Classification of magnetism and altermagnetism in quasicrystals 准晶体中磁性和变磁性的分类

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-09 DOI: 10.1038/s41524-026-02309-1

Zhi-Yan Shao,Jia-Heng Ji,Chen Lu,Zhiming Pan,Yu-Bo Liu,Fan Yang

引用次数: 0

Physics-informed automated surface reconstruction via low-energy electron diffraction based on Bayesian optimization 基于贝叶斯优化的低能电子衍射物理信息自动表面重建

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-09 DOI: 10.1038/s41524-026-02297-2

Xiankang Tang,Ruiwen Xie,Jan P. Hofmann,Hongbin Zhang

{"title":"Physics-informed automated surface reconstruction via low-energy electron diffraction based on Bayesian optimization","authors":"Xiankang Tang,Ruiwen Xie,Jan P. Hofmann,Hongbin Zhang","doi":"10.1038/s41524-026-02297-2","DOIUrl":"https://doi.org/10.1038/s41524-026-02297-2","url":null,"abstract":"Abstract Low-energy electron diffraction (LEED) is a cornerstone technique for determining surface atomic structures 1 , yet the quantitative analysis of electron diffraction intensity as a function of incident electron energy—that is, LEED- I(V) analysis—remains a complex inverse problem. In this work, we tackle quantitative LEED- I(V) analysis based on physics-informed Bayesian optimization (BO). By embedding multiple scattering LEED forward models and crystallographic symmetry for structural parameter pruning directly into a trust-region BO loop, our approach simultaneously optimizes both structural and experimental parameters, adaptively adjusting trust regions for efficient exploration of complex non-convex parameter spaces while substantially reducing manual intervention. The robustness and scalability of the approach are demonstrated using the Ag(100)-(1 × 1) and Fe 2 O 3 (1 $$overline{1}$$ 1 ¯ 02)-(1 × 1) surfaces as examples. Under the same number of samples, the BO workflow achieves faster convergence and lower R-factors than random walk, fast simulated annealing, and genetic algorithm baselines. These results provide a practical and sample-efficient route toward automated LEED- I ( V ) structure reconstruction.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"32 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Accelerating density functional theory calculations using conditional generative-adversarial neural networks 使用条件生成对抗神经网络加速密度泛函理论计算

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-09 DOI: 10.1038/s41524-026-02308-2

Calin-Andrei Pantis-Simut,Nicolae Filipoiu,Andrei Manolescu,George Alexandru Nemnes

引用次数: 0

A set of optimized 3D-MoRSE descriptors for molecular representation 一组优化的三维莫尔斯描述符的分子表示

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-08 DOI: 10.1038/s41524-026-02313-5

Xiaopeng Li,Zhoufa Hu,Ningyi Cui,Yufan Bao,Zhengli Zhang,Guankui Long,Jun-Min Liu,Yecheng Zhou

{"title":"A set of optimized 3D-MoRSE descriptors for molecular representation","authors":"Xiaopeng Li,Zhoufa Hu,Ningyi Cui,Yufan Bao,Zhengli Zhang,Guankui Long,Jun-Min Liu,Yecheng Zhou","doi":"10.1038/s41524-026-02313-5","DOIUrl":"https://doi.org/10.1038/s41524-026-02313-5","url":null,"abstract":"Abstract Accurate early-stage prediction of molecular properties and toxicity is critical for reducing cost and attrition in drug discovery. Here, we develop and evaluate an optimized 3D-MoRSE (OPT3D) molecular representation that introduces a tunable distance-scale factor to better capture informative interatomic distance regimes. With simple traditional machine-learning models, for example, random forest (RF), OPT3D achieves an average (10 times) test root mean squared error (RMSE) of 0.942 (ESOL), 0.940 (Lipophilicity), and 2.108 (FreeSolv), and AUC of 0.824 (BACE), 0.878 (BBBP), 0.621 (SIDER), 0.828 (Tox21), and 0.717 (ToxCast). These results are competitive with recent state-of-the-art predictions that rely on more complex architectures. The prediction performance of this set of descriptors can be further improved by more advanced models: stacked ensembling further reduces regression errors and maintains strong classification performance, and the adaptive checkpointing with specialization neural model can also improve performance relative to simple traditional machine-learning models. Performed perturbation-based tests also show that the OPT3D descriptor exhibits strong robustness under moderate noise. Our results highlight that developing high-quality molecular representations is as important as model innovation, which has been intensively pursued but has yielded limited performance gains.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"24 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 statistical defect models on magnetic material dynamic and static properties 磁性材料动态和静态特性的深度学习统计缺陷模型

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-05 DOI: 10.1038/s41524-026-02285-6

C. Eagan,M. Copus,E. Iacocca

引用次数: 0