npj Computational Materials最新文献_第9页

Microscopic screening theory for excitons in two-dimensional materials: a bridge between effective models and ab initio descriptions 二维材料中激子的微观筛选理论：有效模型与从头算描述之间的桥梁

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-14 DOI: 10.1038/s41524-026-02283-8

Pedro Ninhos, Alejandro J. Uría-Álvarez, Christos Tserkezis, N. Asger Mortensen, Juan José Palacios

{"title":"Microscopic screening theory for excitons in two-dimensional materials: a bridge between effective models and ab initio descriptions","authors":"Pedro Ninhos, Alejandro J. Uría-Álvarez, Christos Tserkezis, N. Asger Mortensen, Juan José Palacios","doi":"10.1038/s41524-026-02283-8","DOIUrl":"https://doi.org/10.1038/s41524-026-02283-8","url":null,"abstract":"We present a computational approach for exciton calculations in two-dimensional (2D) materials within the Bethe–Salpeter equation (BSE) framework, employing an atomistic description with point-like orbitals. Unlike widespread efficient calculations that rely on classical or effective interaction models, such as the Rytova–Keldysh model, our method incorporates quantum screened interactions. By explicitly computing the 2D dielectric function at the random-phase approximation level, we capture screening effects beyond such approximations with an accuracy akin to first-principles methods. Consequently, we can realistically estimate excitonic binding energies with a bearable computational cost. A detailed account of the various convergence parameters sheds light on a possible cause of the large dispersion of binding energies reported in the literature using first-principles GW/BSE implementations. This work thus provides an alternative pathway towards efficient and faithful dielectric screening and exciton computations in low-dimensional materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"18 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148729040","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

MembraneBERT: a domain-tailored NLP model for membrane-based gas separation data extraction 膜分离数据提取的领域定制NLP模型

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-12 DOI: 10.1038/s41524-026-02227-2

Yinyin Cao, Yihong Jiang, Yuhan Yang, Zhongyi Jiang

{"title":"MembraneBERT: a domain-tailored NLP model for membrane-based gas separation data extraction","authors":"Yinyin Cao, Yihong Jiang, Yuhan Yang, Zhongyi Jiang","doi":"10.1038/s41524-026-02227-2","DOIUrl":"https://doi.org/10.1038/s41524-026-02227-2","url":null,"abstract":"Membrane-based separation is a critical technology for energy and environmental processes, positioning it at the forefront of materials science research. To facilitate the data-driven advances in this field, we developed an integrated natural language processing pipeline including a domain-tailored transformer model for literature mining. One major challenge is data imbalance which biases the model toward majority classes and leads to overfitting on dominant patterns. We introduced two complementary strategies to direct the model’s attention toward minority classes: (1) a data augmentation method that prevents data leakage, and (2) model fine-tuning using a customized loss function that integrates class-weighted cross-entropy and focal loss. The resulting model MembraneBERT achieves an average Precision of 0.906, Recall of 0.796, and F1-score of 0.844, respectively. Applied to 3,580 additional articles, the model extracted 2,283 structured entries on membranes and their gas separation performance, offering a practical framework for AI-driven extraction of membrane performance data to support subsequent data-driven research.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"32 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148728975","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

Multireference investigations of ethylene hydrogenation over bimetallic catalysts 双金属催化剂上乙烯加氢的多参比研究

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-12 DOI: 10.1038/s41524-026-02255-y

Rishu Khurana, Matthew R. Hermes, Valay Agarawal, Christopher Knight, Laura Gagliardi, Cong Liu

{"title":"Multireference investigations of ethylene hydrogenation over bimetallic catalysts","authors":"Rishu Khurana, Matthew R. Hermes, Valay Agarawal, Christopher Knight, Laura Gagliardi, Cong Liu","doi":"10.1038/s41524-026-02255-y","DOIUrl":"https://doi.org/10.1038/s41524-026-02255-y","url":null,"abstract":"Antiferromagnetically coupled bimetallic hydrides are promising catalysts for olefin hydrogenation, but their electronic structure presents a challenging problem for single-reference electronic structure methods. We investigated ethylene hydrogenation by unsupported and silica-supported dinuclear Ni and Fe hydride catalysts using density functional theory (DFT) and GPU-accelerated localized active space (LAS) multireference methods and multiconfiguration pair-density functional theory (MC-PDFT). For the unsupported dinuclear Ni hydride catalyst, different DFT functionals predict different spin-state orderings along the catalytic cycle, ethylene binding energetics, transition-state barriers, and even different rate-determining steps. In contrast, multireference methods provide a consistent description of the spin manifold and reaction energetics across the pathway, with substantially reduced sensitivity to the choice of on-top functional. The calculations further show that the bridging Ni-H-Ni motif remains intact during ethylene coordination and migratory insertion, in contrast to previously proposed mechanisms involving prior bridge opening. Comparison of supported and unsupported systems reveals strongly metal-dependent support effects. Silica coordination substantially lowers the hydride-transfer barrier in the Ni catalyst, whereas the hydrogenolysis barrier is elevated for the Fe systems. These results show that antiferromagnetically coupled bimetallic hydrides can fall outside the reliable regime of single-reference DFT and require multireference treatments to obtain consistent mechanistic predictions.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"9 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148729036","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

Towards the discovery of high critical magnetic field superconductors 迈向发现高临界磁场超导体

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-12 DOI: 10.1038/s41524-026-02266-9

Benjamin Geisler, Philip M. Dee, James J. Hamlin, Gregory R. Stewart, Richard G. Hennig, P. J. Hirschfeld

{"title":"Towards the discovery of high critical magnetic field superconductors","authors":"Benjamin Geisler, Philip M. Dee, James J. Hamlin, Gregory R. Stewart, Richard G. Hennig, P. J. Hirschfeld","doi":"10.1038/s41524-026-02266-9","DOIUrl":"https://doi.org/10.1038/s41524-026-02266-9","url":null,"abstract":"Superconducting materials are of significant technological relevance, and intense research efforts aim at enhancing the critical temperature Tc. Intriguingly, while numerous computational and machine-learning approaches have been developed to predict Tc, the fundamental role of the critical magnetic field has so far been overlooked. Here we open a new frontier in superconductor discovery by presenting a consistent computational database of critical fields Hc, Hc1, and Hc2 for ~7300 electron-phonon-mediated superconductors covering diverse materials classes. Combining α2F(ω) spectral functions and accurate Fermi surfaces from density functional theory with clean-limit Eliashberg theory, we compute coherence lengths, London penetration depths, and Ginzburg-Landau parameters. We discover an unexpectedly large number of Type-I superconductors and show that larger unit cells generically support higher critical fields and Type-II behavior. Accounting for strong-coupling and mass-renormalization effects beyond BCS theory proves essential for accurate predictions. The results enable AI-guided inverse-design strategies for high-Tc and high-critical-field superconductors.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"11 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148729037","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

Thermal conductivity of wurtzite Aluminum Nitride under external electric field 纤锌矿氮化铝在外加电场作用下的导热性能

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-11 DOI: 10.1038/s41524-026-02282-9

Joyce Christiansen-Salameh, Samuel Kielar, Han Huang, Zhiting Tian

{"title":"Thermal conductivity of wurtzite Aluminum Nitride under external electric field","authors":"Joyce Christiansen-Salameh, Samuel Kielar, Han Huang, Zhiting Tian","doi":"10.1038/s41524-026-02282-9","DOIUrl":"https://doi.org/10.1038/s41524-026-02282-9","url":null,"abstract":"Ultra-wide bandgap (UWBG) semiconductors host the large critical electric fields necessary to support a leap in the power density of next-generation power and radio-frequency electronics. However, effective thermal management of the heat flux incited by high-voltage operation remains a limiting challenge. The current understanding of thermal transport in UWBG semiconductors neglects the departure from equilibrium driven by electric field ((mathop{E}limits^{ rightharpoonup })), which becomes more severe at higher power density. In this work, we develop a new computational workflow to obtain non-equilibrium ab initio thermal transport properties under (mathop{E}limits^{ rightharpoonup }) within the density-functional theory - non-equilibrium Green’s function (DFT-NEGF) framework. The predicted cross-plane thermal conductivity (k⊥) of wurtzite Aluminum Nitride (AlN) is modified by a cross-plane electric field (({mathop{E}limits^{ rightharpoonup }}_{z})), where the change in k⊥ depends on the direction of ({mathop{E}limits^{ rightharpoonup }}_{z}) with respect to the polarity of the AlN thin film. The findings provide critical insights to understand thermal transport in devices under operating conditions and design effective thermal management for high-power electronics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148729039","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

Improving Spectral Resolution from Real-time Evolution for Correlated Systems 利用实时演化提高相关系统的光谱分辨率

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-11 DOI: 10.1038/s41524-026-02275-8

Ta Tang, Chunjing Jia, Brian Moritz, Thomas P. Devereaux

引用次数: 0

Physics-informed design of drug-excipient nanoparticles via free energy calculation and yoked deep learning 通过自由能计算和轭式深度学习的药物赋形纳米颗粒的物理设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-11 DOI: 10.1038/s41524-026-02262-z

Yan Xiang, Zilu Zhang, Rebeca T. Stiepel, Chinmay S. Potnis, Lauren A. Onweller, Joseph R. Laforet Jr., Hrshita Gowda, Daniel Reker

{"title":"Physics-informed design of drug-excipient nanoparticles via free energy calculation and yoked deep learning","authors":"Yan Xiang, Zilu Zhang, Rebeca T. Stiepel, Chinmay S. Potnis, Lauren A. Onweller, Joseph R. Laforet Jr., Hrshita Gowda, Daniel Reker","doi":"10.1038/s41524-026-02262-z","DOIUrl":"https://doi.org/10.1038/s41524-026-02262-z","url":null,"abstract":"The discovery of self-assembling molecular systems is often constrained by the high computational cost of physics-based simulations and the limited generalizability of data-driven models trained on experimental datasets. This limits our ability to efficiently navigate massive design spaces. Here, we introduce a physics-informed discovery framework that integrates free-energy calculations with sequential-pool active learning and adaptive yoked deep learning to effectively navigate vast chemical co-assembly spaces. Using pairwise free energy as a physically grounded descriptor of co-assembly propensity and excipient net charge as a proxy for interfacial stabilization, our approach predicts nanoparticle-forming drug-excipient combinations without relying on experimental data for model training. Our sequential-pool yoked deep learning strategy utilizes adaptive machine learning to reduce the number of expensive simulations required while maintaining predictive accuracy, enabling screening of 18 million molecular pairs. Prospective validations identified 24 drug nanoparticles using eight previously unknown stabilizing molecules. Representative sorafenib formulations exhibit ultra-high drug loading, effectively improve the aqueous solubility of drugs, and maintain in vitro drug efficacy. Together, this work establishes a generalizable framework coupling molecular simulations and yoked deep learning to accelerate discovery in self-assembling molecular systems, with immediate implications for reformulating poorly soluble drug candidates through rational nanoparticle design.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"15 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148729038","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

Fully compensated ferrimagnet bilayer FeOCl-based multiferroic tunnel junctions 基于feocl的全补偿铁磁体双层多铁隧道结

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-10 DOI: 10.1038/s41524-026-02276-7

Guorong Yu, Bin Liu, Xiao Liu, Keqian He, Shichen Zhang, Sicong Zhu, Lei Shen

{"title":"Fully compensated ferrimagnet bilayer FeOCl-based multiferroic tunnel junctions","authors":"Guorong Yu, Bin Liu, Xiao Liu, Keqian He, Shichen Zhang, Sicong Zhu, Lei Shen","doi":"10.1038/s41524-026-02276-7","DOIUrl":"https://doi.org/10.1038/s41524-026-02276-7","url":null,"abstract":"Although antiferromagnetic (AFM) materials surpass conventional ferromagnetic materials in multiferroic tunnel junctions (MFTJs) due to their ultrafast magnetodynamic response, their application is limited by the difficulty in reading the tunneling magnetoresistance using conventional methods. To address this challenge, we propose breaking the PT symmetry of bilayer FeOCl by vertically flipping. This symmetry-breaking induces spin-splitting in AFM bilayer FeOCl. We then design fully compensated ferrimagnet bilayer FeOCl and antiferroelectric bilayer In2Se3-based tunnel junctions. Using first-principles calculations combined with the non-equilibrium Green’s function method, we demonstrate that various non-volatile resistance states can be realized by adjusting the magnetic state of electrodes and the polarization direction of the ferroelectric barrier. The devices show a high tunneling magnetoresistance (TMR) of 1.8 × 103% and electroresistance (TER) of 1.1 × 105%. This study presents a method to induce spin-splitting in two-dimensional AFM materials and highlights their applications in high-speed and high-density AFM-based spintronic devices.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"16 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148729032","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-based prediction of self-energies from ab initio dynamical mean-field theory for real materials with minimal data sets 基于深度学习的最小数据集实际材料自能动态平均场理论预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-10 DOI: 10.1038/s41524-026-02277-6

Pragati Mitra, Hrishit Banerjee

{"title":"Deep learning-based prediction of self-energies from ab initio dynamical mean-field theory for real materials with minimal data sets","authors":"Pragati Mitra, Hrishit Banerjee","doi":"10.1038/s41524-026-02277-6","DOIUrl":"https://doi.org/10.1038/s41524-026-02277-6","url":null,"abstract":"Density-functional theory (DFT) has been the workhorse of first-principles calculations for decades, and DFT-derived energies and forces are now widely used to train machine learning models of inter-atomic potentials. However, DFT’s single-particle treatment of exchange-correlation functionals severely limits accuracy for materials with open d- and f-shell elements, and ML models trained on such data inherit this limitation. Dynamical mean-field theory (DMFT) addresses this limitation by explicitly incorporating local electronic correlations, albeit at a significantly higher computational cost. In this work, we develop deep-learning models trained on ab-initio DFT+DMFT calculations to predict electronic self-energies from non-interacting Green’s functions. Using the correlated metal SrVO3 as a prototype, we show that accurate self-energy predictions can be achieved from small datasets. Through transfer-learning, models pre-trained on SrVO3 successfully predict the self-energies of CaVO3, BaVO3 and SrNbO3, despite differences in composition and electronic structure. Moreover, models pretrained on SrVO3 and SrNbO3 can predict self-energy of BaNbO3 without training on its self-energy. This approach captures temperature variation, extends beyond d1 perovskites and drastically reduces computational time. These results establish deep-learning as an efficient surrogate for computationally demanding DMFT calculations, enabling rapid prediction of correlation-driven properties, paving the way for a transformative shift in materials theory.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"20 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148728872","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

Screening intrinsic ferroelastic altermagnets in NiAs-type monolayers with anomalous Hall conductivity, magnetoresistance, and elastoresistance 在具有异常霍尔电导率、磁阻和弹性电阻的nias型单层中筛选本征铁弹性交磁体

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-08 DOI: 10.1038/s41524-026-02278-5

Long Zhang, Jianting Dong, Yi Yan, Lixin Zhang, Guangqian Ding, Guangxin Ni, Guoying Gao

{"title":"Screening intrinsic ferroelastic altermagnets in NiAs-type monolayers with anomalous Hall conductivity, magnetoresistance, and elastoresistance","authors":"Long Zhang, Jianting Dong, Yi Yan, Lixin Zhang, Guangqian Ding, Guangxin Ni, Guoying Gao","doi":"10.1038/s41524-026-02278-5","DOIUrl":"https://doi.org/10.1038/s41524-026-02278-5","url":null,"abstract":"Coupling multiferroic orders with emerging altermagnetism offers a promising route toward multifunctional quantum devices. Here, we combine symmetry analysis, systematic density functional theory screening, and nonequilibrium Green’s function calculations to investigate 48 NiAs-type (110) monolayers. We identify a class of ferroelastic-altermagnetic biferroics exhibiting intrinsic coupling between lattice strain and spin order. Specifically, metallic FeN and semiconducting MnTe monolayers emerge as representative systems, showing reversible ferroelastic switching with low barriers of 0.08 and 0.22 eV per formula unit and accessible signal intensity. FeN exhibits above-room-temperature (334 K) altermagnetism while MnTe possesses Néel temperature of 122 K. Both monolayers display ferroelastic-reversible momentum-dependent spin splitting (245 and 307 meV). This coupling enables tunable anomalous Hall conductivity and strain-controlled electronic transport. Prototype device-level simulations reveal magnetoresistance (454%), elastoresistance (155%), and high spin-filtering efficiency (-87%). These findings establish NiAs-type monolayers as a versatile platform for intrinsic biferroic coupling, providing a foundation for high-density, energy-efficient spintronic architectures.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"7 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148693469","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