npj Computational Materials最新文献_第6页

Chiral soft mode transition driven by strain in ferroelectric bubble domains 铁电泡域中应变驱动的手性软模跃迁

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-05 DOI: 10.1038/s41524-026-02311-7

Urmimala Dey,Natalya S. Fedorova,Jorge Íñiguez-González,Hugo Aramberri

引用次数: 0

Accelerated stable structure prediction of Li-intercalated bilayer graphene using a data-efficient deep learning framework 利用数据高效的深度学习框架加速锂嵌入双层石墨烯的稳定结构预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-04 DOI: 10.1038/s41524-026-02314-4

Haoran Li,Bilin Gui,He Zhang,Le Yang,Hao-Sen Chen

引用次数: 0

Field-aware and charge-informed machine learning for predicting molecular and condensed-phase responses and vibrational spectra 用于预测分子和凝聚相响应和振动光谱的场感知和电荷信息机器学习

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-04 DOI: 10.1038/s41524-026-02293-6

Ke Chen,Sandra Luber

{"title":"Field-aware and charge-informed machine learning for predicting molecular and condensed-phase responses and vibrational spectra","authors":"Ke Chen,Sandra Luber","doi":"10.1038/s41524-026-02293-6","DOIUrl":"https://doi.org/10.1038/s41524-026-02293-6","url":null,"abstract":"Abstract External electric fields play a crucial role in chemistry, materials, and biology. However, accurately capturing external field effects remains a challenge for machine learning interatomic potentials (MLIPs). By revealing the limitations of conventional architectures in predicting field-dependent response properties, we introduce a physics-informed field-aware equivariant neural network framework that predicts response properties to external electric fields. External electric fields are incorporated through an equivariant embedding layer that concatenates field representations with atomic features, preserving rotational equivariance. The model leverages an energy-derivative approach to predict response properties in a single unified architecture. Furthermore, we embed a charge-equilibration electrostatic layer into the model to capture long-range electrostatic interactions. The close agreement between charged-based and energy derivative-based electric dipoles indicates that the model learns the correct underlying physics. The unified framework performs robustly for the prediction of response properties and simulation of IR and Raman spectra across molecular and periodic systems under different external field strengths. By incorporating physical principles into the model, we establish a general and transferable framework for modeling molecular and condensed-phase response under external electric fields, providing a robust routine for response properties prediction in external electric fields.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"27 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893666","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

From global optimization to transition state search: an automated workflow for surface reaction barriers 从全局优化到过渡状态搜索：表面反应障碍的自动化工作流程

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-04 DOI: 10.1038/s41524-026-02298-1

Hyunwook Jung,Emanuel Colombi Manzi,Tiago J. Goncalves,Vanessa J. Bukas,Sandip De,Johannes T. Margraf,Karsten Reuter,Hendrik H. Heenen

{"title":"From global optimization to transition state search: an automated workflow for surface reaction barriers","authors":"Hyunwook Jung,Emanuel Colombi Manzi,Tiago J. Goncalves,Vanessa J. Bukas,Sandip De,Johannes T. Margraf,Karsten Reuter,Hendrik H. Heenen","doi":"10.1038/s41524-026-02298-1","DOIUrl":"https://doi.org/10.1038/s41524-026-02298-1","url":null,"abstract":"Abstract Reaction barrier calculations present the major bottleneck in the systematic exploration of surface reaction networks in heterogeneous catalysis via atomistic simulations. Each transition-state search introduces a high-dimensional configuration space of initial and final-state combinations that must be explored to identify the minimum-energy pathway with the lowest activation barrier. Typically, this task is delegated to human chemical intuition as user input to, e.g., nudged elastic band (NEB) calculations, since its automation remains challenging. Here, we introduce NEBscape, a fully automated workflow for transition-state searches that requires no human intervention. Initial and final states are generated via global optimization and systematically combined through atom mapping, symmetry alignment, and heuristic selection to yield a tractable set of NEB calculations. We benchmark NEBscape against diverse reactions and model surfaces from the OC20NEB database, where it identifies, on average, lower transition-state energies and produces minimum-energy pathways with high chemical fidelity to the target reactions.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"354 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893665","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

Evolving transition-state search with agentic large language models 基于代理大语言模型的演化过渡状态搜索

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-03 DOI: 10.1038/s41524-026-02301-9

Jan A. Meissner,Philipp Kuboth,Jan Meisner

{"title":"Evolving transition-state search with agentic large language models","authors":"Jan A. Meissner,Philipp Kuboth,Jan Meisner","doi":"10.1038/s41524-026-02301-9","DOIUrl":"https://doi.org/10.1038/s41524-026-02301-9","url":null,"abstract":"Abstract Accurate identification of transition states (TSs) is fundamental to computational chemistry. Modern reaction-discovery efforts increasingly rely on curating and completing large reaction datasets, where even a small fraction of TS-search failures can leave key pathways unresolved and bias the resulting reaction network. Here, we introduce an adaptive evolutionary framework for TS search workflows that prioritizes completion of a target dataset over global algorithmic robustness. The framework iteratively focuses on reactions for which no validated TS has been found and dynamically modifies the workflow to search the remaining TSs. Rather than converging toward a single universally optimal TS search workflow, the framework generates a sequence of specialized workflow variants that collectively increase TS coverage across the dataset. Applied to the Transition1X benchmark (over 10,000 reactions), this adaptive evolution improves a state-of-the-art TS-search workflow to achieve a >80% success rate of TS-finding with rigorous validation via eigenvector analysis and reaction path endpoint confirmation. More broadly, these results suggest that adaptive, LLM-driven evolutionary workflow optimization provides a transferable strategy for improving validated TS coverage in large-scale, failure-prone scientific workflows.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893667","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

Multiscale modelling of ferroelectrics using a physics-informed neural network driven by molecular dynamics data: parameter identification and field reconstruction 利用分子动力学数据驱动的物理信息神经网络的铁电体多尺度建模：参数识别和场重建

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-02 DOI: 10.1038/s41524-026-02300-w

Xuejian Wang,Frank Wendler,Hikaru Auzuma,Michael Zaiser,Shuji Ogata,Ryo Kobayashi,Lei Zeng,Xingchen Tan

{"title":"Multiscale modelling of ferroelectrics using a physics-informed neural network driven by molecular dynamics data: parameter identification and field reconstruction","authors":"Xuejian Wang,Frank Wendler,Hikaru Auzuma,Michael Zaiser,Shuji Ogata,Ryo Kobayashi,Lei Zeng,Xingchen Tan","doi":"10.1038/s41524-026-02300-w","DOIUrl":"https://doi.org/10.1038/s41524-026-02300-w","url":null,"abstract":"Abstract In multiscale modeling of ferroelectric materials, a fundamental challenge is to transfer discrete atomistic information into a continuum phase-field model (PFM) while retaining an accurate description of mesoscale behavior. In this paper, a physics-informed neural network (PINN) framework is developed, in which the loss function consists of MD-data penalty and partial difference equation (PDE) constraints. By minimizing the loss function, the model not only reconstructs the polarization field along with the full coupled electromechanical response, including strain, stress, electric field, and energy landscape at the continuum scale, but also identifies critical parameters required for the PFM, including the characteristic energy density, characteristic length factor, anisotropy factor, and Landau polynomial coefficients. By using the PINN-predicted parameters to solve the corresponding PDEs within a finite element framework, we perform a cross-verification showing that the two numerical implementations yield consistent results. In addition, the transferability of the learned parameters is further evaluated through extended three-dimensional PF simulations under external tensile and bending loading, in which the domain expansion, shrinkage, annihilation, and fragmentation are in agreement with the corresponding experimental observations. This framework provides an effective methodology for multiscale bridging between atomistic and continuum descriptions for perovskite ferroelectric materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"53 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893668","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

Machine learning driven multi-property prediction for rare earth permanent magnet materials 机器学习驱动的稀土永磁材料多属性预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-01 DOI: 10.1038/s41524-026-02299-0

Dan Liu, Yuan Li, Xinqi Zheng, Xin Ming, Ruoshui Liu, Jiazheng Hao, Yinong Yin, Jianfeng Xi, Tongyun Zhao, Fengxia Hu, Baogen Shen

{"title":"Machine learning driven multi-property prediction for rare earth permanent magnet materials","authors":"Dan Liu, Yuan Li, Xinqi Zheng, Xin Ming, Ruoshui Liu, Jiazheng Hao, Yinong Yin, Jianfeng Xi, Tongyun Zhao, Fengxia Hu, Baogen Shen","doi":"10.1038/s41524-026-02299-0","DOIUrl":"https://doi.org/10.1038/s41524-026-02299-0","url":null,"abstract":"Rare earth permanent magnets are key functional materials for strategic emerging fields such as energy engineering and intelligent manufacturing. Misch metal (MM)‑based alloys help boost rare‑earth resource utilization, yet traditional trial‑and‑error experiments suffer from long cycles and cumbersome variable control. In this work, we established 18 model-optimization combinations by coupling six machine learning models with three hyperparameter optimization algorithms. A systematic test was conducted on datasets with different target properties, including Curie temperature Tc, residual magnetization Br, saturation magnetization Ms, coercivity Hc and maximum energy product (BH)max. For Tc prediction, 14 combinations achieved the coefficient of determination R2 > 0.9. For the small-sample datasets Br and Ms, 8 combinations maintained R2 > 0.79 with satisfying generalization capacity. The optimal combination, Extreme Gradient Boosting coupled with Grey Wolf Optimizer, was further applied to predict Hc and (BH)max of MMxFeyB100-x-y. The high-performance regions for Hc and (BH)max are x = 0.14 – 0.16, y = 0.79 – 0.80 and x = 0.125 – 0.13, y = 0.79 – 0.795. Its predictions closely matched experimental data, with average errors of merely 0.317 kOe and 0.016 MGOe. These findings provide guidance on the machine learning driven material design and performance optimization for permanent magnets.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"385 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899508","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 physics-informed synthetic-to-experimental framework for few-shot structural segmentation of HRTEM images 基于物理信息的HRTEM图像少镜头结构分割合成-实验框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-01 DOI: 10.1038/s41524-026-02306-4

Shengmin Zhou, Xuehai Huang, Junjie Lin, Yu Wang

{"title":"A physics-informed synthetic-to-experimental framework for few-shot structural segmentation of HRTEM images","authors":"Shengmin Zhou, Xuehai Huang, Junjie Lin, Yu Wang","doi":"10.1038/s41524-026-02306-4","DOIUrl":"https://doi.org/10.1038/s41524-026-02306-4","url":null,"abstract":"High-resolution transmission electron microscopy (HRTEM) can reveal nanoscale structural heterogeneity, yet converting weak and ambiguous image contrast into reproducible multiclass structural maps remains difficult because reliable pixel-level annotations are scarce. Existing HRTEM segmentation workflows therefore often remain limited to binary foreground-background separation or other simplified tasks, rather than simultaneous parsing of crystalline regions, grain boundaries, amorphous or structurally unclear regions, and background. Here we report a physics-informed synthetic-to-experimental framework that enables few-shot multiclass HRTEM segmentation. The key advance is a Domain Construction-Relaxation-Imaging (DCRI) framework that generates synthetic HRTEM images with strictly co-registered structural labels, providing scalable supervision for synthetic pretraining. In this two-stage workflow, DCRI pretraining provides transferable structural priors, while few-shot experimental fine-tuning adapts these priors to real HRTEM images. Using Au nanoparticles as the primary model system, we show that this strategy produces coherent four-class segmentation under low-label conditions, whereas experimental-only training remains unstable. The benefit is retained across representative U-Net-family backbones and after few-shot adaptation to Au STEM images, shows partial transfer to annotated PbS HRTEM images, and further supports SAM-based foundation-model adaptation. These results establish a physics-informed synthetic-to-experimental framework as a practical route to alleviate the annotation bottleneck in few-shot structural segmentation of HRTEM images.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"48 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899507","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

Electrically driven topological magnetic knots in BiFeO3 films BiFeO3薄膜中的电驱动拓扑磁结

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-01 DOI: 10.1038/s41524-026-02279-4

Shouzhe Dong, Chengchao Hu, Changqing Guo, Ke Xu, Xiaoming Shi, Jing Wang, Houbing Huang

引用次数: 0

A viable and accurate route to GW simulations of metals and doped semiconductors 一个可行的和准确的途径，以GW模拟金属和掺杂半导体

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-01 DOI: 10.1038/s41524-026-02268-7

Giacomo Sesti, Alberto Guandalini, Andrea Ferretti, Pino D’Amico, Claudia Cardoso, Massimo Rontani, Daniele Varsano

{"title":"A viable and accurate route to GW simulations of metals and doped semiconductors","authors":"Giacomo Sesti, Alberto Guandalini, Andrea Ferretti, Pino D’Amico, Claudia Cardoso, Massimo Rontani, Daniele Varsano","doi":"10.1038/s41524-026-02268-7","DOIUrl":"https://doi.org/10.1038/s41524-026-02268-7","url":null,"abstract":"In this work, we present an efficient and fully ab initio approach for the calculation of the screened Coulomb potential, specifically designed for two-dimensional (2D) and three-dimensional (3D) metals and suitable to evaluate GW corrections. While quasiparticle renormalization effects are typically negligible in bulk metals, 2D metals may exhibit semiconductor-like features, such as sizeable renormalization of quasiparticle energies induced by Coulomb interaction, which follow from the suppression of screening due to the reduced dimensionality. Although this effect can in principle be described by the GW method, apart from a few pioneering exceptions, such calculations have been hampered by sampling requirements exceeding feasibility. We demonstrate that the combination of interpolation and extrapolation schemes together with the accounting of dynamical effects is pivotal to the correct handling of the intraband polarizability in the long-wavelength limit. As an application, we report the gap renormalization of selected doped 2D semiconductors in excellent agreement with ARPES measurements.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"11 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899509","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