Pub Date : 2026-01-08DOI: 10.1038/s41524-025-01915-9
Youheng Chen, Jiajia Han, Chen Yang, Cuiping Wang, Xingjun Liu
The stacking fault energy (SFE) in concentrated alloys is highly sensitive to the local atomic environment. Understanding this relationship necessitates extensive sampling of configurations, which significantly increases computational demands. To address this challenge, we propose a graph neural network (GNN)-based framework utilizing unrelaxed bulk and stacking fault structures as inputs to directly predict SFE. We first investigate two key extrapolation capabilities for bulk formation energy prediction: scale extrapolation (predicting formation energy in larger supercells) and compositional extrapolation (predicting formation energy for compositions beyond the training set). Leveraging the validated model, we concurrently predict formation energies of both bulk and stacking fault configurations to compute the SFE. The structural similarity between these configurations enables efficient parameter sharing, accelerating model convergence. The framework demonstrates excellent interpretability and robust compositional extrapolation capabilities in predicting SFEs. Furthermore, leveraging its exceptional compositional extrapolation, we integrate the model with Monte Carlo simulations to successfully predict ordering behavior and solute segregation at stacking faults. Finally, we introduce a hierarchical training strategy that further reduces data requirements. Collectively, our work establishes a unified and efficient framework for robust prediction of planar fault energies in complex concentrated alloys.
{"title":"A crystal graph convolutional neural network framework for predicting stacking fault energy in concentrated alloys","authors":"Youheng Chen, Jiajia Han, Chen Yang, Cuiping Wang, Xingjun Liu","doi":"10.1038/s41524-025-01915-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01915-9","url":null,"abstract":"The stacking fault energy (SFE) in concentrated alloys is highly sensitive to the local atomic environment. Understanding this relationship necessitates extensive sampling of configurations, which significantly increases computational demands. To address this challenge, we propose a graph neural network (GNN)-based framework utilizing unrelaxed bulk and stacking fault structures as inputs to directly predict SFE. We first investigate two key extrapolation capabilities for bulk formation energy prediction: scale extrapolation (predicting formation energy in larger supercells) and compositional extrapolation (predicting formation energy for compositions beyond the training set). Leveraging the validated model, we concurrently predict formation energies of both bulk and stacking fault configurations to compute the SFE. The structural similarity between these configurations enables efficient parameter sharing, accelerating model convergence. The framework demonstrates excellent interpretability and robust compositional extrapolation capabilities in predicting SFEs. Furthermore, leveraging its exceptional compositional extrapolation, we integrate the model with Monte Carlo simulations to successfully predict ordering behavior and solute segregation at stacking faults. Finally, we introduce a hierarchical training strategy that further reduces data requirements. Collectively, our work establishes a unified and efficient framework for robust prediction of planar fault energies in complex concentrated alloys.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"3 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145919889","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}
Pub Date : 2026-01-07DOI: 10.1038/s41524-025-01927-5
Anvesh Nathani, Arthur RC McCray, Yingtao Liu, Hanping Ding, Pejman Kazempoor, Shuozhi Xu, Colin Ophus, Iman Ghamarian
Electron diffraction(ED) often used to solve for unknown structures or refine existing ones. Existing methods for automated ED analysis often struggle with challenges such as computational expense and experimental noise. This study introduces a deep learning framework to accelerate and improve crystal structure determination from diffraction patterns. The methodology treats each diffraction pattern as a relational graph of Bragg spots. Spot features are encoded using a 1D convolutional network, from which a relational attention aggregator constructs an orientation-agnostic graph. This graph is processed by a Graphormer encoder enhanced with Mixture-of-Experts layers, allowing the model to learn complex crystallographic relationships efficiently. Trained and tested on a large dataset of simulated diffraction patterns, the model achieved a crystal system classification accuracy of 89.2% and a space group accuracy of 70.2% from single patterns, significantly outperforming a state-of-the-art random forest baseline (74.2% and 57.8%, respectively). By aggregating predictions across multiple zone axes, these accuracies improved to 96.5% and 79.5%. The model also demonstrated robust performance on experimental data of gold nanoparticles, producing plausible classifications consistent with known orientation degeneracies. By unifying relational graph reasoning with specialized expert networks, this work presents a robust and automated framework for high-throughput materials characterization.
{"title":"Accelerating electron diffraction analysis using graph neural networks and attention mechanisms","authors":"Anvesh Nathani, Arthur RC McCray, Yingtao Liu, Hanping Ding, Pejman Kazempoor, Shuozhi Xu, Colin Ophus, Iman Ghamarian","doi":"10.1038/s41524-025-01927-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01927-5","url":null,"abstract":"Electron diffraction(ED) often used to solve for unknown structures or refine existing ones. Existing methods for automated ED analysis often struggle with challenges such as computational expense and experimental noise. This study introduces a deep learning framework to accelerate and improve crystal structure determination from diffraction patterns. The methodology treats each diffraction pattern as a relational graph of Bragg spots. Spot features are encoded using a 1D convolutional network, from which a relational attention aggregator constructs an orientation-agnostic graph. This graph is processed by a Graphormer encoder enhanced with Mixture-of-Experts layers, allowing the model to learn complex crystallographic relationships efficiently. Trained and tested on a large dataset of simulated diffraction patterns, the model achieved a crystal system classification accuracy of 89.2% and a space group accuracy of 70.2% from single patterns, significantly outperforming a state-of-the-art random forest baseline (74.2% and 57.8%, respectively). By aggregating predictions across multiple zone axes, these accuracies improved to 96.5% and 79.5%. The model also demonstrated robust performance on experimental data of gold nanoparticles, producing plausible classifications consistent with known orientation degeneracies. By unifying relational graph reasoning with specialized expert networks, this work presents a robust and automated framework for high-throughput materials characterization.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"391 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145908188","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}
Pub Date : 2026-01-07DOI: 10.1038/s41524-025-01923-9
Daye Zheng, Xingliang Peng, Yike Huang, Yinan Wang, Duo Zhang, Zhengtao Huang, Zefeng Cai, Linfeng Zhang, Mohan Chen, Ben Xu, Weiqing Zhou
We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic phenomena that requires large-scale simulations at the atomic level. First, we propose a basis-independent projection method for calculating atomic magnetic moments by applying a radial truncation to numerical atomic orbitals. A double-loop Lagrange multiplier method is utilized to ensure the satisfaction of constraint conditions while achieving accurate magnetic torque. The method is implemented in ABACUS with both plane wave basis and numerical atomic orbital basis. We benchmark the iron (Fe) systems and analyze differences from calculations with the plane wave basis and numerical atomic orbitals basis in describing magnetic energy barriers. Based on an automated workflow composed of first-principles calculations, magnetic model, active learning, and dynamics simulation, more than 30,000 first-principles data with the information of magnetic torque are generated to train a deep-learning-based magnetic model DeePSPIN for the Fe system. By utilizing the model in large-scale molecular dynamics simulations, we successfully predict Curie temperatures of α-Fe close to experimental values.
{"title":"Integrating deep-learning-based magnetic model and non-collinear spin-constrained method: methodology, implementation and application","authors":"Daye Zheng, Xingliang Peng, Yike Huang, Yinan Wang, Duo Zhang, Zhengtao Huang, Zefeng Cai, Linfeng Zhang, Mohan Chen, Ben Xu, Weiqing Zhou","doi":"10.1038/s41524-025-01923-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01923-9","url":null,"abstract":"We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic phenomena that requires large-scale simulations at the atomic level. First, we propose a basis-independent projection method for calculating atomic magnetic moments by applying a radial truncation to numerical atomic orbitals. A double-loop Lagrange multiplier method is utilized to ensure the satisfaction of constraint conditions while achieving accurate magnetic torque. The method is implemented in ABACUS with both plane wave basis and numerical atomic orbital basis. We benchmark the iron (Fe) systems and analyze differences from calculations with the plane wave basis and numerical atomic orbitals basis in describing magnetic energy barriers. Based on an automated workflow composed of first-principles calculations, magnetic model, active learning, and dynamics simulation, more than 30,000 first-principles data with the information of magnetic torque are generated to train a deep-learning-based magnetic model DeePSPIN for the Fe system. By utilizing the model in large-scale molecular dynamics simulations, we successfully predict Curie temperatures of α-Fe close to experimental values.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"28 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145908460","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}
Suppressing critical current density ( Jc ) fluctuations in Josephson junctions is essential for improving the reproducibility and scalability of superconducting quantum processors. Despite many elucidations of microscopic mechanisms, the physical modulation of Jc by atomic-scale disorder at the metal-insulator interface remains elusive. Here, we reveal that interfacial bonding topology distortions are the dominant source that regulates Jc uniformity. We identify a new disorder metric, Interface Bonding Topology Factor (IBTF), that captures bond-angle fluctuations and oxygen-coordination heterogeneity within Jc variations. Through multivariate analysis, Jc is exponentially correlated with interface disorder and barrier thickness ( d ) by Jc ∝ e −IBTF⋅ d , explaining 91.88% of the observed Jc inhomogeneity. We establish IBTF as a tunable physical degree of freedom whose suppression efficacy enhances significantly with increasing d , and demonstrate its active modulation by twin boundary engineering in electrodes. This work provides a device-oriented strategy and a tunable physical metric beyond single-feature control for scalable high-performance quantum processors.
{"title":"Revealing the role of interface disorder in modulating critical current density of Josephson junctions","authors":"Chuanbing Han, Huihui Sun, Yonglong Shen, Junling Qiu, Peng Xu, Fudong Liu, Bo Zhao, Xiaohan Yu, Weilong Wang, Shuya Wang, Qing Mu, Benzheng Yuan, Lixin Wang, Chaofeng Hou, Zheng Shan","doi":"10.1038/s41524-025-01941-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01941-7","url":null,"abstract":"Suppressing critical current density ( <jats:italic>J</jats:italic> <jats:sub>c</jats:sub> ) fluctuations in Josephson junctions is essential for improving the reproducibility and scalability of superconducting quantum processors. Despite many elucidations of microscopic mechanisms, the physical modulation of <jats:italic>J</jats:italic> <jats:sub>c</jats:sub> by atomic-scale disorder at the metal-insulator interface remains elusive. Here, we reveal that interfacial bonding topology distortions are the dominant source that regulates <jats:italic>J</jats:italic> <jats:sub>c</jats:sub> uniformity. We identify a new disorder metric, Interface Bonding Topology Factor (IBTF), that captures bond-angle fluctuations and oxygen-coordination heterogeneity within <jats:italic>J</jats:italic> <jats:sub>c</jats:sub> variations. Through multivariate analysis, <jats:italic>J</jats:italic> <jats:sub>c</jats:sub> is exponentially correlated with interface disorder and barrier thickness ( <jats:italic>d</jats:italic> ) by <jats:italic>J</jats:italic> <jats:sub>c</jats:sub> ∝ <jats:italic>e</jats:italic> <jats:sup> −IBTF⋅ <jats:italic>d</jats:italic> </jats:sup> , explaining 91.88% of the observed <jats:italic>J</jats:italic> <jats:sub>c</jats:sub> inhomogeneity. We establish IBTF as a tunable physical degree of freedom whose suppression efficacy enhances significantly with increasing <jats:italic>d</jats:italic> , and demonstrate its active modulation by twin boundary engineering in electrodes. This work provides a device-oriented strategy and a tunable physical metric beyond single-feature control for scalable high-performance quantum processors.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"31 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903107","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}
Pub Date : 2026-01-06DOI: 10.1038/s41524-025-01869-y
Miguel Angel Moreno-Mateos, Paul Steinmann
Cutting soft materials is a complex process governed by the interplay of bulk large deformation, interfacial soft fracture, and contact forces with the cutting tool. Existing experimental characterizations and numerical models often fail to capture the variety of observed cutting behaviors, especially the transition from indentation to cutting and the roles of dissipative mechanisms. Here, we combine novel experimental cutting tests on three representative materials—a soft hydrogel, an elastomer, and food materials—with a coupled computational model that integrates soft fracture, adhesion, and frictional interactions. Our experiments reveal material-dependent cutting behaviors, with abrupt or smooth transitions from indentation to crack initiation, followed by distinct steady cutting regimes. The computational model captures these behaviors and shows that adhesion and damping contributions in the cohesive forces dominate tangential stresses, while Coulomb friction plays a negligible role due to low contact pressures. Together, these results provide new mechanistic insights into the physics of soft cutting and offer a unified framework for soft cutting mechanics to guide the design of soft materials, cutting tools, and cutting protocols, with direct relevance to surgical dissection and the engineering of food textures optimized for mastication.
{"title":"Cutting soft materials: how material differences shape the response","authors":"Miguel Angel Moreno-Mateos, Paul Steinmann","doi":"10.1038/s41524-025-01869-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01869-y","url":null,"abstract":"Cutting soft materials is a complex process governed by the interplay of bulk large deformation, interfacial soft fracture, and contact forces with the cutting tool. Existing experimental characterizations and numerical models often fail to capture the variety of observed cutting behaviors, especially the transition from indentation to cutting and the roles of dissipative mechanisms. Here, we combine novel experimental cutting tests on three representative materials—a soft hydrogel, an elastomer, and food materials—with a coupled computational model that integrates soft fracture, adhesion, and frictional interactions. Our experiments reveal material-dependent cutting behaviors, with abrupt or smooth transitions from indentation to crack initiation, followed by distinct steady cutting regimes. The computational model captures these behaviors and shows that adhesion and damping contributions in the cohesive forces dominate tangential stresses, while Coulomb friction plays a negligible role due to low contact pressures. Together, these results provide new mechanistic insights into the physics of soft cutting and offer a unified framework for soft cutting mechanics to guide the design of soft materials, cutting tools, and cutting protocols, with direct relevance to surgical dissection and the engineering of food textures optimized for mastication.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"34 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903516","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}
Pub Date : 2026-01-06DOI: 10.1038/s41524-025-01935-5
Pavel B. Sorokin, Boris I. Yakobson
{"title":"The properties, thermodynamics and application prospects of diamanes","authors":"Pavel B. Sorokin, Boris I. Yakobson","doi":"10.1038/s41524-025-01935-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01935-5","url":null,"abstract":"","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"43 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145908182","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}
Pub Date : 2026-01-06DOI: 10.1038/s41524-025-01942-6
Jingya Zhang, Yin Zhang
Solid solution strengthening is a key mechanism for enhancing the strength of high-entropy alloys (HEAs). However, conventional strengthening theories fail to capture the complex environments in HEAs. Here, we present a data-driven framework to investigate the composition-dependent intrinsic strength of FCC HEAs. Using large-scale molecular dynamics simulations, we compute dislocation mobility under various temperatures and compositions, revealing jerky and wavy glide behavior due to fluctuating local pinning. The critical resolved shear stress (CRSS) at 0 K is extracted from these data, and a linear correlation is revealed between CRSS and the standard deviation of atomic pinning strength. Then, we propose atomic features describing local structural and compositional fluctuations and construct a symbolic model to predict the atomic pinning strength variability from these features, using the Sure Independence Screening and Sparsifying Operator method. This framework provides both mechanistic insight and predictive capability for the design of strong, compositionally complex alloys.
{"title":"Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis","authors":"Jingya Zhang, Yin Zhang","doi":"10.1038/s41524-025-01942-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01942-6","url":null,"abstract":"Solid solution strengthening is a key mechanism for enhancing the strength of high-entropy alloys (HEAs). However, conventional strengthening theories fail to capture the complex environments in HEAs. Here, we present a data-driven framework to investigate the composition-dependent intrinsic strength of FCC HEAs. Using large-scale molecular dynamics simulations, we compute dislocation mobility under various temperatures and compositions, revealing jerky and wavy glide behavior due to fluctuating local pinning. The critical resolved shear stress (CRSS) at 0 K is extracted from these data, and a linear correlation is revealed between CRSS and the standard deviation of atomic pinning strength. Then, we propose atomic features describing local structural and compositional fluctuations and construct a symbolic model to predict the atomic pinning strength variability from these features, using the Sure Independence Screening and Sparsifying Operator method. This framework provides both mechanistic insight and predictive capability for the design of strong, compositionally complex alloys.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"30 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903738","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}
In material design, traditional crystal structure prediction approaches are expensive as they require extensive structural sampling through expensive energy minimization methods. Emerging artificial intelligence (AI) generative models have shown great promise in rapidly generating realistic crystals, but they typically handle only a few tens of atoms per unit cell. To overcome this limitation, we introduce a symmetry-informed approach, the Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal). Our method generates initial structures using AI models trained on an augmented dataset, and then optimizes them using structure descriptors rather than energy-based optimization. We demonstrate its effectiveness by expanding from 25 known low-energy sp2 carbon allotropes to over 1700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and battery materials.
{"title":"AI-assisted rapid crystal structure generation towards a target local environment","authors":"Osman Goni Ridwan, Sylvain Pitié, Monish Soundar Raj, Dong Dai, Gilles Frapper, Hongfei Xue, Qiang Zhu","doi":"10.1038/s41524-025-01931-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01931-9","url":null,"abstract":"In material design, traditional crystal structure prediction approaches are expensive as they require extensive structural sampling through expensive energy minimization methods. Emerging artificial intelligence (AI) generative models have shown great promise in rapidly generating realistic crystals, but they typically handle only a few tens of atoms per unit cell. To overcome this limitation, we introduce a symmetry-informed approach, the Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal). Our method generates initial structures using AI models trained on an augmented dataset, and then optimizes them using structure descriptors rather than energy-based optimization. We demonstrate its effectiveness by expanding from 25 known low-energy sp2 carbon allotropes to over 1700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and battery materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"18 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903745","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}
Pub Date : 2026-01-05DOI: 10.1038/s41524-025-01926-6
M. Cepeda-Arancibia, F. Brevis, S. J. R. Holt, D. Cortés-Ortuño, H. Fangohr, P. Landeros
Chiral spin textures in ferromagnetic materials with Dzyaloshinskii-Moriya interactions (DMIs) have attracted significant interest in recent years owing to their potential applications in nanodevices. This work focuses on describing stable conical-helix configurations hosted in ultrathin films with DMI and perpendicular anisotropy. These states are studied for different kinds of DMIs, including symmetry classes <jats:inline-formula> <jats:alternatives> <jats:tex-math>$${mathcal{T}}$$</jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> </jats:alternatives> </jats:inline-formula> , <jats:inline-formula> <jats:alternatives> <jats:tex-math>$${{mathcal{C}}}_{nv}$$</jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> <mml:mi>v</mml:mi> </mml:mrow> </mml:msub> </mml:math> </jats:alternatives> </jats:inline-formula> , isotropic and anisotropic <jats:inline-formula> <jats:alternatives> <jats:tex-math>$${{mathcal{D}}}_{2d}$$</jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>D</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>d</mml:mi> </mml:mrow> </mml:msub> </mml:math> </jats:alternatives> </jats:inline-formula> , <jats:inline-formula> <jats:alternatives> <jats:tex-math>$${{mathcal{D}}}_{n}$$</jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>D</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:msub> </mml:math> </jats:alternatives> </jats:inline-formula> , <jats:inline-formula> <jats:alternatives> <jats:tex-math>$${{mathcal{C}}}_{n}$$</jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:msub> </mml:math> </jats:alternatives> </jats:inline-formula> , and <jats:inline-formula> <jats:alternatives> <jats:tex-math>$${{mathcal{S}}}_{4}$$</jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>S</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:math> </jats:alternatives> </jats:inline-formula> . A parameterised analytical model of these configurations is proposed, enabling the determination of optimal parameters characterising the magnetic texture, such as the pitch vector or nucleation field. To substantiate the results, micromagnetic simulations are developed for comparison with the theoretical solutions. Numerical solutions are optimised by implementing finite-difference codes that use next-nearest neighbours and explicit Robin boundary conditions stemming from symmetric exchange and DMI. It is shown that these numerical enhancements decrease anisotropic effects in helical solutions. This study establishes a method to analyse conical-helix textures in thin-film systems with
近年来，具有Dzyaloshinskii-Moriya相互作用的铁磁材料的手性自旋织构由于其在纳米器件中的潜在应用而引起了人们的极大兴趣。这项工作的重点是描述具有DMI和垂直各向异性的超薄膜中稳定的锥形螺旋结构。这些状态研究了不同类型的dmi，包括对称类$${mathcal{T}}$$ T, $${{mathcal{C}}}_{nv}$$ C n v，各向同性和各向异性$${{mathcal{D}}}_{2d}$$ d2 D, $${{mathcal{D}}}_{n}$$ D n, $${{mathcal{C}}}_{n}$$ C n和$${{mathcal{S}}}_{4}$$ s4。提出了这些构型的参数化分析模型，从而确定表征磁性织构的最佳参数，如节距矢量或成核场。为了证实结果，进行了微磁模拟，并与理论解进行了比较。数值解决方案是通过实现有限差分代码，使用下近邻和明确的罗宾边界条件源于对称交换和DMI优化。结果表明，这些数值增强降低了螺旋解的各向异性效应。本研究建立了一种分析具有任意DMI的薄膜系统中的锥形螺旋织构的方法，使用本文开发的开放获取代码可以以更高的精度模拟。
{"title":"Micromagnetics of conical-helix textures in thin films with different kinds of Dzyaloshinskii-Moriya interactions","authors":"M. Cepeda-Arancibia, F. Brevis, S. J. R. Holt, D. Cortés-Ortuño, H. Fangohr, P. Landeros","doi":"10.1038/s41524-025-01926-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01926-6","url":null,"abstract":"Chiral spin textures in ferromagnetic materials with Dzyaloshinskii-Moriya interactions (DMIs) have attracted significant interest in recent years owing to their potential applications in nanodevices. This work focuses on describing stable conical-helix configurations hosted in ultrathin films with DMI and perpendicular anisotropy. These states are studied for different kinds of DMIs, including symmetry classes <jats:inline-formula> <jats:alternatives> <jats:tex-math>$${mathcal{T}}$$</jats:tex-math> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mi>T</mml:mi> </mml:math> </jats:alternatives> </jats:inline-formula> , <jats:inline-formula> <jats:alternatives> <jats:tex-math>$${{mathcal{C}}}_{nv}$$</jats:tex-math> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> <mml:mi>v</mml:mi> </mml:mrow> </mml:msub> </mml:math> </jats:alternatives> </jats:inline-formula> , isotropic and anisotropic <jats:inline-formula> <jats:alternatives> <jats:tex-math>$${{mathcal{D}}}_{2d}$$</jats:tex-math> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msub> <mml:mrow> <mml:mi>D</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>d</mml:mi> </mml:mrow> </mml:msub> </mml:math> </jats:alternatives> </jats:inline-formula> , <jats:inline-formula> <jats:alternatives> <jats:tex-math>$${{mathcal{D}}}_{n}$$</jats:tex-math> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msub> <mml:mrow> <mml:mi>D</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:msub> </mml:math> </jats:alternatives> </jats:inline-formula> , <jats:inline-formula> <jats:alternatives> <jats:tex-math>$${{mathcal{C}}}_{n}$$</jats:tex-math> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:msub> </mml:math> </jats:alternatives> </jats:inline-formula> , and <jats:inline-formula> <jats:alternatives> <jats:tex-math>$${{mathcal{S}}}_{4}$$</jats:tex-math> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msub> <mml:mrow> <mml:mi>S</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:math> </jats:alternatives> </jats:inline-formula> . A parameterised analytical model of these configurations is proposed, enabling the determination of optimal parameters characterising the magnetic texture, such as the pitch vector or nucleation field. To substantiate the results, micromagnetic simulations are developed for comparison with the theoretical solutions. Numerical solutions are optimised by implementing finite-difference codes that use next-nearest neighbours and explicit Robin boundary conditions stemming from symmetric exchange and DMI. It is shown that these numerical enhancements decrease anisotropic effects in helical solutions. This study establishes a method to analyse conical-helix textures in thin-film systems with ","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"27 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903108","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：