Pub Date : 2026-03-11DOI: 10.1038/s41524-026-02020-1
Chen Qian, Valdas Vitartas, James R. Kermode, Reinhard J. Maurer
Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band structures and density of states. For large-scale applications, an ideal model would exhibit high generalization ability and computational efficiency. Here, we introduce the MACE-H graph neural network, which combines high body-order message passing with a node-order expansion to efficiently obtain all relevant O(3) irreducible representations. The model achieves high accuracy and computational efficiency and captures the full local chemical environment features of, currently, up to f orbital matrix interaction blocks. We demonstrate the model’s accuracy and transferability on several open materials benchmark datasets of two-dimensional materials and a new dataset for bulk gold, achieving sub-meV prediction errors on matrix elements and high accuracy on eigenvalues across all systems. We further analyze the interplay of high-body-order message passing and locality that makes this model a good candidate for high-throughput material screening.
{"title":"Equivariant electronic Hamiltonian prediction with many-body message passing","authors":"Chen Qian, Valdas Vitartas, James R. Kermode, Reinhard J. Maurer","doi":"10.1038/s41524-026-02020-1","DOIUrl":"https://doi.org/10.1038/s41524-026-02020-1","url":null,"abstract":"Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band structures and density of states. For large-scale applications, an ideal model would exhibit high generalization ability and computational efficiency. Here, we introduce the MACE-H graph neural network, which combines high body-order message passing with a node-order expansion to efficiently obtain all relevant O(3) irreducible representations. The model achieves high accuracy and computational efficiency and captures the full local chemical environment features of, currently, up to f orbital matrix interaction blocks. We demonstrate the model’s accuracy and transferability on several open materials benchmark datasets of two-dimensional materials and a new dataset for bulk gold, achieving sub-meV prediction errors on matrix elements and high accuracy on eigenvalues across all systems. We further analyze the interplay of high-body-order message passing and locality that makes this model a good candidate for high-throughput material screening.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"53 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394051","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 : 2026-03-10DOI: 10.1038/s41524-026-02027-8
Vahidullah Taç, Amirhossein Amiri-Hezaveh, Grace N. Bechtel, Titus Loftin, Manuel K. Rausch, Francisco Sahli Costabal, Adrian Buganza Tepole
Accurately identifying the mechanical behavior of heterogeneous materials is a central challenge in materials science, with implications for the design of composites, metamaterials, and engineered biological tissue. Conventional inverse methods require closed-form constitutive models and are often restricted to simplified geometries or homogeneous properties, limiting their ability to capture complex, spatially varying material responses. Here, we introduce a fully data-driven framework for inverse characterization that recovers the complete constitutive behavior of heterogeneous solids directly from full-field displacement data, without prescribing a specific material law. Our approach combines neural ordinary differential equation (NODE) constitutive models, which inherently satisfy key thermodynamic and mathematical constraints, with a hyper-network that maps each material point to its local NODE, enabling continuous representation of arbitrary spatial variation in material properties. The loss function at the center of the method includes the strong form of equilibrium and traction boundary conditions. We demonstrate the method’s robustness on synthetic datasets, including heterogeneous isotropic and anisotropic materials, noise-contaminated measurements, and complex geometries, and validate it with digital image correlation experiments on 3D-printed elastomers. This framework provides a general, physically consistent route to inferring heterogeneous constitutive behavior from experimental data, offering new opportunities for accurate mechanical characterization across a broad range of material systems.
{"title":"Fully data-driven inverse characterization of heterogeneous materials with hyper-network neural ODEs","authors":"Vahidullah Taç, Amirhossein Amiri-Hezaveh, Grace N. Bechtel, Titus Loftin, Manuel K. Rausch, Francisco Sahli Costabal, Adrian Buganza Tepole","doi":"10.1038/s41524-026-02027-8","DOIUrl":"https://doi.org/10.1038/s41524-026-02027-8","url":null,"abstract":"Accurately identifying the mechanical behavior of heterogeneous materials is a central challenge in materials science, with implications for the design of composites, metamaterials, and engineered biological tissue. Conventional inverse methods require closed-form constitutive models and are often restricted to simplified geometries or homogeneous properties, limiting their ability to capture complex, spatially varying material responses. Here, we introduce a fully data-driven framework for inverse characterization that recovers the complete constitutive behavior of heterogeneous solids directly from full-field displacement data, without prescribing a specific material law. Our approach combines neural ordinary differential equation (NODE) constitutive models, which inherently satisfy key thermodynamic and mathematical constraints, with a hyper-network that maps each material point to its local NODE, enabling continuous representation of arbitrary spatial variation in material properties. The loss function at the center of the method includes the strong form of equilibrium and traction boundary conditions. We demonstrate the method’s robustness on synthetic datasets, including heterogeneous isotropic and anisotropic materials, noise-contaminated measurements, and complex geometries, and validate it with digital image correlation experiments on 3D-printed elastomers. This framework provides a general, physically consistent route to inferring heterogeneous constitutive behavior from experimental data, offering new opportunities for accurate mechanical characterization across a broad range of material systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"6 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381762","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 : 2026-03-10DOI: 10.1038/s41524-026-02024-x
R. A. W. Ayyubi, Seyfal Sultanov, James P. Buban, Robert F. Klie
Atomic-scale defects govern many functional properties of materials, yet their systematic identification and quantification remain challenging because supervised learning approaches require extensive labeled datasets, which are scarce in atomic-resolution microscopy due to the complexity and diversity of defect structures. To overcome this limitation, we introduce a fully unsupervised machine learning framework capable of discovering and clustering defect structures without prior labeling or predefined defect classes. The framework employs a convolutional variational autoencoder (CVAE) to reconstruct ideal, defect-free images, enabling the generation of difference images that isolate local structural anomalies. From these, 47 features are extracted and refined through a three-tier feature selection process to minimize redundancy and noise. Dimensionality reduction via principal component analysis (PCA), combined with silhouette score optimization, guides the determination of the optimal cluster number prior to applying k-means clustering, which yields well-separated groups corresponding to distinct defect types. Validated on CdTe and SrTiO3 datasets, this unsupervised, label-free approach enables high-throughput defect discovery and clustering in scanning transmission electron microscopy (STEM) and related imaging modalities.
{"title":"Unsupervised defect clustering in atomic-resolution microscopy using a convolutional variational autoencoder","authors":"R. A. W. Ayyubi, Seyfal Sultanov, James P. Buban, Robert F. Klie","doi":"10.1038/s41524-026-02024-x","DOIUrl":"https://doi.org/10.1038/s41524-026-02024-x","url":null,"abstract":"Atomic-scale defects govern many functional properties of materials, yet their systematic identification and quantification remain challenging because supervised learning approaches require extensive labeled datasets, which are scarce in atomic-resolution microscopy due to the complexity and diversity of defect structures. To overcome this limitation, we introduce a fully unsupervised machine learning framework capable of discovering and clustering defect structures without prior labeling or predefined defect classes. The framework employs a convolutional variational autoencoder (CVAE) to reconstruct ideal, defect-free images, enabling the generation of difference images that isolate local structural anomalies. From these, 47 features are extracted and refined through a three-tier feature selection process to minimize redundancy and noise. Dimensionality reduction via principal component analysis (PCA), combined with silhouette score optimization, guides the determination of the optimal cluster number prior to applying k-means clustering, which yields well-separated groups corresponding to distinct defect types. Validated on CdTe and SrTiO3 datasets, this unsupervised, label-free approach enables high-throughput defect discovery and clustering in scanning transmission electron microscopy (STEM) and related imaging modalities.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381763","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 : 2026-03-09DOI: 10.1038/s41524-026-02017-w
Matteo Masto, Vincent Favre-Nicolin, Steven Leake, Tobias Schülli, Marie-Ingrid Richard, Clement Atlan, Ewen Bellec
In Bragg Coherent Diffraction Imaging (BCDI), Phase Retrieval of highly strained crystals is often challenging with standard iterative algorithms. This computational obstacle limits the potential of the technique as it precludes the reconstruction of physically interesting, highly-strained particles. Here, we propose a novel approach to this problem using a supervised Convolutional Neural Network (CNN) trained on 3D simulated diffraction data to predict the corresponding reciprocal space phase. This method allows to fully exploit the potential of the CNN by mapping functions within the same space and leveraging structural similarities between input and output. The final object is obtained by the inverse Fourier transform of the retrieved complex diffracted amplitude and is then further refined with iterative algorithms. We demonstrate that our model outperforms standard algorithms on highly strained simulated data not included in the training set, as well as on experimental data.
{"title":"Phase retrieval of highly strained Bragg coherent diffraction patterns using supervised convolutional neural network","authors":"Matteo Masto, Vincent Favre-Nicolin, Steven Leake, Tobias Schülli, Marie-Ingrid Richard, Clement Atlan, Ewen Bellec","doi":"10.1038/s41524-026-02017-w","DOIUrl":"https://doi.org/10.1038/s41524-026-02017-w","url":null,"abstract":"In Bragg Coherent Diffraction Imaging (BCDI), Phase Retrieval of highly strained crystals is often challenging with standard iterative algorithms. This computational obstacle limits the potential of the technique as it precludes the reconstruction of physically interesting, highly-strained particles. Here, we propose a novel approach to this problem using a supervised Convolutional Neural Network (CNN) trained on 3D simulated diffraction data to predict the corresponding reciprocal space phase. This method allows to fully exploit the potential of the CNN by mapping functions within the same space and leveraging structural similarities between input and output. The final object is obtained by the inverse Fourier transform of the retrieved complex diffracted amplitude and is then further refined with iterative algorithms. We demonstrate that our model outperforms standard algorithms on highly strained simulated data not included in the training set, as well as on experimental data.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"52 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381764","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 : 2026-03-07DOI: 10.1038/s41524-026-02009-w
Viviana Dovale-Farelo, Pedram Tavadze, Miguel A. L. Marques, Srinjoy Das, Kamal Choudhary, Alejandro Bautista-Hernández, Aldo H. Romero
This work investigates how reparametrizing the Strongly Constrained and Appropriately Normed (SCAN) exchange-correlation (XC) functional within density functional theory affects predictions of the electronic bandgap (Eg) for solids. A system dependent functional (SD-SCAN) is proposed by adjusting a subset of SCAN’s internal parameters to improve bandgaps. For most covalent materials, SD-SCAN yields bandgaps closer to experimental values while preserving accurate lattice constants; improvements remain limited for highly ionic systems, reflecting constraints of SCAN’s α-dependence and the absence of long-range nonlocal (Hartree-Fock-like) exchange at the semilocal/meta-GGA level. The modified parameters enhance exchange in regions with covalent character, raising the conduction bands and broadening the charge density, thereby yielding more realistic electronic structures and improved dielectric response. A machine-learning model (ML-SCAN) predicts SCAN parameters from solid-state descriptors, providing a flexible, system-dependent reparametrization strategy competitive with existing semilocal approaches. A simplified variant, SCAN-0.2, offers a fixed-parameter shortcut for improved bandgap calculations. Overall, this study lays the groundwork for ML-driven XC functionals for semiconductors.
{"title":"System-conditioned reparameterization of the SCAN functional for accurate bandgaps: from analytical constraints to machine learning","authors":"Viviana Dovale-Farelo, Pedram Tavadze, Miguel A. L. Marques, Srinjoy Das, Kamal Choudhary, Alejandro Bautista-Hernández, Aldo H. Romero","doi":"10.1038/s41524-026-02009-w","DOIUrl":"https://doi.org/10.1038/s41524-026-02009-w","url":null,"abstract":"This work investigates how reparametrizing the Strongly Constrained and Appropriately Normed (SCAN) exchange-correlation (XC) functional within density functional theory affects predictions of the electronic bandgap (Eg) for solids. A system dependent functional (SD-SCAN) is proposed by adjusting a subset of SCAN’s internal parameters to improve bandgaps. For most covalent materials, SD-SCAN yields bandgaps closer to experimental values while preserving accurate lattice constants; improvements remain limited for highly ionic systems, reflecting constraints of SCAN’s α-dependence and the absence of long-range nonlocal (Hartree-Fock-like) exchange at the semilocal/meta-GGA level. The modified parameters enhance exchange in regions with covalent character, raising the conduction bands and broadening the charge density, thereby yielding more realistic electronic structures and improved dielectric response. A machine-learning model (ML-SCAN) predicts SCAN parameters from solid-state descriptors, providing a flexible, system-dependent reparametrization strategy competitive with existing semilocal approaches. A simplified variant, SCAN-0.2, offers a fixed-parameter shortcut for improved bandgap calculations. Overall, this study lays the groundwork for ML-driven XC functionals for semiconductors.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"4 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371117","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 : 2026-03-06DOI: 10.1038/s41524-026-02028-7
Shubin Wen, Ren-Ci Peng, Xiaoxing Cheng, Min Liao, Yichun Zhou
The dynamic behavior of ferroelectric domain walls (DWs), particularly both 90° and 180° DWs, is crucial for high-performance HfO2-based ferroelectric devices. However, fundamentally understanding DW dynamics is challenging because the role of 90° DWs and their interplay with 180° DWs in ferroelectric switching remains elusive in HfO2-based ferroelectrics. Here, we employ phase-field simulations to investigate the dynamics of domain and DW in epitaxial Hf0.5Zr0.5O2 thin films with the coexistence of 90° and 180° DWs. It indicates that the threshold voltage for 90° DW migration is much higher than that for 180° DW owing to the higher migration energy barrier for the former. 90° DWs play a complex dual role in ferroelectric switching: they lower the nucleation voltage by serving as preferential nucleation sites for 180° domain switching, while simultaneously impeding the propagation of 180° DWs due to their high migration energy barrier. Furthermore, 90° DWs guide the switching pathway of nascent 180° domains around ferroelastic domains to avoid the formation of unstable charged DWs. These findings provide a fundamental mesoscale understanding of competitive and synergistic mechanisms between 90° and 180° DWs in ferroelectric switching, offering guidance for precise manipulation of DWs to optimize the performance of HfO2-based ferroelectric memories.
{"title":"The dual role of 90° domain walls in ferroelectric switching of Hf0.5Zr0.5O2 thin films: Insights from phase-field simulations","authors":"Shubin Wen, Ren-Ci Peng, Xiaoxing Cheng, Min Liao, Yichun Zhou","doi":"10.1038/s41524-026-02028-7","DOIUrl":"https://doi.org/10.1038/s41524-026-02028-7","url":null,"abstract":"The dynamic behavior of ferroelectric domain walls (DWs), particularly both 90° and 180° DWs, is crucial for high-performance HfO2-based ferroelectric devices. However, fundamentally understanding DW dynamics is challenging because the role of 90° DWs and their interplay with 180° DWs in ferroelectric switching remains elusive in HfO2-based ferroelectrics. Here, we employ phase-field simulations to investigate the dynamics of domain and DW in epitaxial Hf0.5Zr0.5O2 thin films with the coexistence of 90° and 180° DWs. It indicates that the threshold voltage for 90° DW migration is much higher than that for 180° DW owing to the higher migration energy barrier for the former. 90° DWs play a complex dual role in ferroelectric switching: they lower the nucleation voltage by serving as preferential nucleation sites for 180° domain switching, while simultaneously impeding the propagation of 180° DWs due to their high migration energy barrier. Furthermore, 90° DWs guide the switching pathway of nascent 180° domains around ferroelastic domains to avoid the formation of unstable charged DWs. These findings provide a fundamental mesoscale understanding of competitive and synergistic mechanisms between 90° and 180° DWs in ferroelectric switching, offering guidance for precise manipulation of DWs to optimize the performance of HfO2-based ferroelectric memories.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371120","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 : 2026-03-06DOI: 10.1038/s41524-026-02032-x
Diego Ibarra-Hoyos, Peter F. Connors, Ho Jang, Nathan Grain, Israel Klich, Gia-Wei Chern, Peter K. Liaw, John R. Scully, Joseph Poon
Data scarcity remains a central challenge in materials discovery, where finding meaningful descriptors and tuning models for generalization is critical but inherently a discrete optimization problem prone to multiple local minima confounding the true optimal state. Classical methods often become trapped in these minima, while quantum annealing can escape them via quantum fluctuations, including tunneling, which overcome narrow energy barriers. We present a quantum-assisted machine-learning (QaML) framework that employs quantum annealing to address these combinatorial-optimization challenges through feature selection, support-vector training formulated in QUBO form for classification and regression, and a new QUBO-based neural-network pruning formulation. Recursive batching enables quantum annealing to manage large feature spaces beyond current qubit limits, while quantum-pruned networks exhibit superior generalization over classical methods, suggesting that quantum annealing preferentially samples flatter, more stable regions of the loss landscape. Applied to high-entropy alloys (HEAs), a data-limited but compositionally complex testbed, the framework integrates models for the fracture-strain classification and yield-strength regression under physics-based constraints. The framework identified and experimentally validated Al8Cr38Fe50Mn2Ti2 (at.%), a single-phase BCC alloy exhibiting a 0.2% yield strength of 568 MPa, greater than 40% compressive strain without fracture, and a critical current density in reducing acid nearly an order of magnitude lower than 304 stainless steel. These results establish QA as a practical route to overcome classical optimization limits and accelerate materials discovery.
{"title":"Quantum-annealed machine learning discovers ductile, high strength and corrosion-resistant high-entropy alloy","authors":"Diego Ibarra-Hoyos, Peter F. Connors, Ho Jang, Nathan Grain, Israel Klich, Gia-Wei Chern, Peter K. Liaw, John R. Scully, Joseph Poon","doi":"10.1038/s41524-026-02032-x","DOIUrl":"https://doi.org/10.1038/s41524-026-02032-x","url":null,"abstract":"Data scarcity remains a central challenge in materials discovery, where finding meaningful descriptors and tuning models for generalization is critical but inherently a discrete optimization problem prone to multiple local minima confounding the true optimal state. Classical methods often become trapped in these minima, while quantum annealing can escape them via quantum fluctuations, including tunneling, which overcome narrow energy barriers. We present a quantum-assisted machine-learning (QaML) framework that employs quantum annealing to address these combinatorial-optimization challenges through feature selection, support-vector training formulated in QUBO form for classification and regression, and a new QUBO-based neural-network pruning formulation. Recursive batching enables quantum annealing to manage large feature spaces beyond current qubit limits, while quantum-pruned networks exhibit superior generalization over classical methods, suggesting that quantum annealing preferentially samples flatter, more stable regions of the loss landscape. Applied to high-entropy alloys (HEAs), a data-limited but compositionally complex testbed, the framework integrates models for the fracture-strain classification and yield-strength regression under physics-based constraints. The framework identified and experimentally validated Al8Cr38Fe50Mn2Ti2 (at.%), a single-phase BCC alloy exhibiting a 0.2% yield strength of 568 MPa, greater than 40% compressive strain without fracture, and a critical current density in reducing acid nearly an order of magnitude lower than 304 stainless steel. These results establish QA as a practical route to overcome classical optimization limits and accelerate materials discovery.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371132","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 : 2026-03-06DOI: 10.1038/s41524-026-02022-z
Yonghao Zhu, Wen-Hao Liu, Run Long, Lin-Wang Wang, Jun-Wei Luo, Zhi Wang
Remanent polarization and coercive field in ferroelectrics are often predicted to be high, yet experimentally observed to be much lower-an inconsistency that hinders the rational design of functional materials and devices. We identify a hidden mechanism underlying this discrepancy: the interaction between polarization domain walls (PDWs) and lattice domain walls (LDWs) that standard models omit. Using κ-Ga2O3 as a representative ferroelectric, we develop a machine-learning potential trained on ab initio molecular-dynamics data to capture realistic polarization switching. Our simulations reveal that PDWs become topologically blocked at 120° LDWs, stabilizing residual domain-wall networks that suppress remanent polarization while enabling rapid, low-field switching by bypassing slow nucleation. The blocking strengthens as lattice domains shrink, offering a new strategy for tuning ferroelectric performance through lattice-domain engineering. The mechanism not only reconciles theoretical with experimental results but also provides a practical approach for improving ferroelectric performance.
{"title":"Origin of suppressed ferroelectricity in κ-Ga2O3: interplay between polarization and lattice domain walls","authors":"Yonghao Zhu, Wen-Hao Liu, Run Long, Lin-Wang Wang, Jun-Wei Luo, Zhi Wang","doi":"10.1038/s41524-026-02022-z","DOIUrl":"https://doi.org/10.1038/s41524-026-02022-z","url":null,"abstract":"Remanent polarization and coercive field in ferroelectrics are often predicted to be high, yet experimentally observed to be much lower-an inconsistency that hinders the rational design of functional materials and devices. We identify a hidden mechanism underlying this discrepancy: the interaction between polarization domain walls (PDWs) and lattice domain walls (LDWs) that standard models omit. Using κ-Ga2O3 as a representative ferroelectric, we develop a machine-learning potential trained on ab initio molecular-dynamics data to capture realistic polarization switching. Our simulations reveal that PDWs become topologically blocked at 120° LDWs, stabilizing residual domain-wall networks that suppress remanent polarization while enabling rapid, low-field switching by bypassing slow nucleation. The blocking strengthens as lattice domains shrink, offering a new strategy for tuning ferroelectric performance through lattice-domain engineering. The mechanism not only reconciles theoretical with experimental results but also provides a practical approach for improving ferroelectric performance.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"32 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371122","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 : 2026-03-06DOI: 10.1038/s41524-026-02031-y
Siya Zhu, Hagen Eckert, Stefano Curtarolo, Jan Schroers, Raymundo Arróyave, Axel van de Walle
Seemingly identical Bulk Metallic Glasses (BMG) often exhibit strikingly different mechanical properties despite having the same composition and fictive temperature. A postulated mechanism underlying these differences is the presence of “defects” and density variations. Motivated by this perspective, we introduce physically realistic and quantitatively controllable density fluctuations in molecular dynamics simulations to systematically examine their role in shear band formation under applied stress. We find that the critical shear strain is strongly dependent on the magnitude and size of the fluctuations, revealing a nonlinear activation behavior associated with localized rejuvenation. This finding also elucidates why, historically, critical shear stresses obtained in simulations have differed so much from those found experimentally, as typical simulations setups might favor unrealistically uniform geometries.
{"title":"Computational study of density fluctuation-facilitated shear band formation in bulk metallic glasses","authors":"Siya Zhu, Hagen Eckert, Stefano Curtarolo, Jan Schroers, Raymundo Arróyave, Axel van de Walle","doi":"10.1038/s41524-026-02031-y","DOIUrl":"https://doi.org/10.1038/s41524-026-02031-y","url":null,"abstract":"Seemingly identical Bulk Metallic Glasses (BMG) often exhibit strikingly different mechanical properties despite having the same composition and fictive temperature. A postulated mechanism underlying these differences is the presence of “defects” and density variations. Motivated by this perspective, we introduce physically realistic and quantitatively controllable density fluctuations in molecular dynamics simulations to systematically examine their role in shear band formation under applied stress. We find that the critical shear strain is strongly dependent on the magnitude and size of the fluctuations, revealing a nonlinear activation behavior associated with localized rejuvenation. This finding also elucidates why, historically, critical shear stresses obtained in simulations have differed so much from those found experimentally, as typical simulations setups might favor unrealistically uniform geometries.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"103 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371121","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 : 2026-03-06DOI: 10.1038/s41524-026-02005-0
Aikaterini Vriza, Michael H. Prince, Tao Zhou, Henry Chan, Mathew J. Cherukara
Advanced scientific user facilities, such as next generation X-ray light sources and self-driving laboratories, are revolutionizing scientific discovery by automating routine tasks and enabling rapid experimentation and characterizations. However, these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for more intricate instruments and experiments. This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Large Language Models (LLMs), as a transformative tool to achieve this goal. We present our approach to developing a human-in-the-loop pipeline for operating advanced instruments including an X-ray nanoprobe beamline and an autonomous robotic station dedicated to the design and characterization of materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex, multi-task workflows, which also include multimodal data, optimizing their performance through optional human input and iterative learning. We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities.
{"title":"Operating advanced scientific instruments with AI agents that learn on the job","authors":"Aikaterini Vriza, Michael H. Prince, Tao Zhou, Henry Chan, Mathew J. Cherukara","doi":"10.1038/s41524-026-02005-0","DOIUrl":"https://doi.org/10.1038/s41524-026-02005-0","url":null,"abstract":"Advanced scientific user facilities, such as next generation X-ray light sources and self-driving laboratories, are revolutionizing scientific discovery by automating routine tasks and enabling rapid experimentation and characterizations. However, these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for more intricate instruments and experiments. This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Large Language Models (LLMs), as a transformative tool to achieve this goal. We present our approach to developing a human-in-the-loop pipeline for operating advanced instruments including an X-ray nanoprobe beamline and an autonomous robotic station dedicated to the design and characterization of materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex, multi-task workflows, which also include multimodal data, optimizing their performance through optional human input and iterative learning. We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"26 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371119","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}
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